Cultural adaptation and validity testing of the Health Literacy Questionnaire (HLQ) in Hindi in northern India

Cultural adaptation and validity testing of the Health Literacy Questionnaire (HLQ) in Hindi in northern India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cultural adaptation and validity testing of the Health Literacy Questionnaire (HLQ) in Hindi in northern India Reetu Passi, Rajesh Kumar, Richard H Osborne, Melanie Hawkins, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8340440/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Health literacy is an important determinant of health. To assess health literacy, the Health Literacy Questionnaire (HLQ) was developed in 2013 in Australia. However, for the 9-scale HLQ to be used in a different socio-cultural environment, it is necessary to culturally adapt and test the questionnaire before implementation. This study aimed to adapt the HLQ to Hindi and evaluate its psychometric properties in an Indian rural, resource-poor setting, the Chandigarh Union Territory. Methods The translation was guided by the Translation Integrity Protocol, followed by a consensus meeting with the HLQ developer. Cognitive interviews were undertaken to collect validity evidence on response process. A cross-sectional survey was then conducted to evaluate the psychometric properties, including internal structure and reliability, of the HLQ Hindi version. Results Cognitive interviews (n = 10) results indicated that revision to one term was required. A total of 260 adults participated in the survey (mean age: 36.9 years) with 61.5% being women. All one-factor confirmatory factor models demonstrated satisfactory to reasonable fit except Scale 3 ‘Actively managing my health’ which achieved excellent fit following model adjustment. Factor loadings were all > 0.60 except for three items. The nine-factor model demonstrated close fit (χ² WLSMV (866) = 1371, p = 0.000, CFI = 0.970, TLI = 0.967, RMSEA = 0.047, SRMR = 0.053). Insufficient discriminant validity was observed among most of the nine factors. Reliability was good, with Raykov’s composite reliability > 0.70 for Scales 1 to 5 and > 0.80 for Scales 6 to 9. Conclusions The culturally adapted HLQ Hindi version was found to have strong psychometric properties in the Indian rural setting. It will be a valuable needs assessment tool to improve health equity and outcomes. Health literacy Health Literacy Questionnaire HLQ validity rural setting India psychometric testing Introduction Health literacy is a multidimensional concept that is now having wide impact in public health planning and intervention development. The concept of health literacy has progressed from measurement of reading, comprehension and numeracy skills [ 1 , 2 ] to people’s ability and resources to access, understand, appraise, remember and use health information in ways that maintain good health and well-being [ 3 ]. The World Health Organization (WHO) recognizes that health literacy incudes how people build knowledge and competencies to manage their health through everyday experiences, social connections, and interactions with health services [ 3 , 4 ]. As such, health literacy is not only related to an individual’s abilities but also reflects the ability and responsiveness of health services to ensure equitable access to, and use of health services [ 3 ]. The nine-dimension Health Literacy Questionnaire (HLQ) was developed using a grounded approach to derive health literacy concepts directly from the lived experience of health service users and a diverse range of Australian and international health practitioners in 2012 [ 5 ]. The HLQ is now being used in more than 90 countries and has been translated and adapted into 47 language versions with validity testing studies undertaken in Europe [ 6 – 10 ], Asia [ 11 , 12 ], South America [ 13 ] and Africa [ 14 ]. Measuring health literacy can provide insights into people’s lived experiences, strengths, and challenges as they attempt to use health information and navigate health systems that are shaped by local norms, entitlements, and practices [ 3 ]. Given this complexity, applying measures developed in other settings without testing whether people interpret and respond to items as intended, risks producing misleading data and overlooking the knowledge and capabilities of communities [ 15 ]. Therefore, validity testing is necessary to ensure that interpretations of scores from a health literacy measure are conceptually and culturally appropriate for a context, such as India, and that actions and decisions based on score interpretations are contextually meaningful and equitable [ 15 , 16 ]. In a country where Hindi is the most widely spoken language, adapting and testing the Hindi version of the HLQ using qualitative and quantitative methods supports measurement that is locally grounded, promotes equity, and avoids the ethical risk of epistemic injustice [ 15 ]. The validity evidence will determine if the Hindi HLQ can accurately capture health literacy strengths and challenges in the Indian rural setting to inform the design of locally relevant health interventions. Methods The present study aimed to culturally adapt and examine the validity evidence on response process, internal structure and reliability of the Hindi version of the HLQ among adults living in a village of northern India. This study was a mixed-method study, involving two phases. In the first phase, we translated and culturally adapted the HLQ into Hindi and conducted cognitive interviews to investigate the degree to which the participants interpreted and responded to the items as intended by the questionnaire developers (response process evidence). For the second phase, we undertook a cross-sectional survey to evaluate the tool’s internal structure, including floor and ceiling effects, item difficulty, dimensionality based on confirmatory factor analysis (CFA), and discriminant validity, and reliability. Setting and participants A village from the Chandigarh Union Territory of India was selected purposively for this study. The village has been a training area for students from the National Institute of Nursing Education, Post-Graduate Institute of Medical Education and Research (PGIMER), Chandigarh. As part of their community health training, nursing students regularly visit the village to provide health education, basic care, and support to local families. This existing relationship helped the researcher (RP) engage with the community more effectively and facilitate smooth data collection. Participants for this study were aged 18 years and above, with no upper age limit. We included participants who could speak and understand Hindi. Since we did not assess literacy formally, we used an interview-based approach if participants had difficulty reading the questionnaire. For the cognitive interviewing, we selected a sample size of 10 based on previous HLQ cognitive testing studies and the literature, which have shown this number to be sufficient for identifying major issues with item interpretation and response [ 17 – 19 ]. For the testing of CFA using the weighted least squares mean and variance adjusted (WSLMV) estimator for categorical data as in this study, sample size recommendations may range from 200 to above [ 20 – 22 ]. Considering the size and accessibility of the population at the data collection site, a sample size of 260 was estimated. Participant recruitment In India, every village is served by a primary healthcare facility staffed by key healthcare providers including a Medical Officer and Multi-Purpose Health Worker(s) (MPHW). These personnel maintain regular contact with the community and are familiar with the village layout. To design the recruitment procedure, we consulted the Medical Officer and MPHW of the Civil Dispensary in the selected village to help identify the village’s sub-areas and support access to households. The study village had eight sub-areas. Out of the eight sub-areas, houses were numbered in only six sub-areas, and the numbered houses were selected for the study. In the six selected sub-areas, there were a total of 2,024 houses. A proportionate random sampling technique was used to select houses in each sub-area. Using computer-generated random numbers, we selected the required number of houses from each sub-area. The sampled houses were approached according to their serial order in the sampling list. For the cognitive interviews, one adult participant was selected from each of 10 randomly selected households within these six sub-areas. For the cross-sectional survey, assuming that two adults lived in each house, a total of 130 houses were selected to reach the sample size of 260 adults. In households with more than one eligible adult, participants were interviewed separately by different interviewers, or each participant was provided with a questionnaire to complete separately to minimize clustering effect, i.e., influence on each other’s responses. All adults from the sampled houses were recruited in the survey until the required sample size of 260 was reached. In cases where a house was found locked or residents were unavailable over two consecutive visits from researchers, the house on the right side of the sampled house was selected for inclusion. The Health Literacy Questionnaire (HLQ) A license to use and adapt the HLQ into Hindi was obtained by PGIMER from the HLQ authors (L1777IA). The HLQ consists of nine health literacy domains [ 5 ]. See Table 1 for the HLQ scales and descriptions for the interpretation of higher and lower scores [ 23 ]. Table 1 The Health Literacy Questionnaire (HLQ) scales with descriptions of higher and lower scores Higher HLQ score Lower HLQ score Part 1: Scales 1 to 5 Score range: 1 to 4 (1 = Strongly disagree, 2 = Disagree, 3 = Agree, 4 = Strongly agree) 1. Feeling understood and supported by healthcare providers (4 items) Has an established relationship with at least one healthcare provider who knows them well. The person trusts this provider to give useful advice and information and to assist them to understand information and make decisions about their health. Is unable to engage with doctors and other healthcare providers. The person doesn’t have a regular healthcare provider and/or has difficulty trusting healthcare providers as a source of information and/or advice. 2. Having sufficient information to manage my health (4 items) Feels confident that they have all the information that they need to live with and manage their condition and to make decisions. Feels that there are many gaps in their knowledge and that they don’t have the information they need to live with and manage their health concerns. 3. Actively managing my health (5 items) Recognises the importance of and can take responsibility for their health. The person proactively engages in their care and makes their own decisions about their health. Doesn’t see their health as their responsibility. The person is not engaged in their health care and regards health care as something that is done to them. 4. Social support for health (5 items) The person’s social system provides them with all the support they want or need. Completely alone and unsupported. 5. Appraisal of health information (5 items) Able to identify good information and reliable sources of information. They can resolve conflicting information by themselves or with help from others. No matter how hard they try, the person cannot understand most health information and becomes confused when there is conflicting information. Part 2: Scales 6 to 9 Score range: 1 to 5 (1 = Cannot do or always difficult, 2 = Usually difficult, 3 = Sometimes difficult, 4 = Usually easy, 5 = Always easy) 6. Ability to actively engage with healthcare providers (5 items) Is proactive about their health and feels in control in relationships with healthcare providers. Is able to seek advice from additional healthcare providers when necessary. Keeps going until they get what they want. Empowered. Is passive in their approach to health care, inactive, does not seek or clarify information and advice and/or service options. Accepts information without question. Unable to ask questions to get information or to clarify what they don’t understand. Accepts what is offered without seeking to ensure it meets their needs. Feels unable to share concerns. 7. Navigating the healthcare system (6 items) Able to find out about services and supports so they get all their needs met. Able to advocate on their own behalf at the system and service level. Unable to advocate on their own behalf and unable to find someone who can help them use the healthcare system to address their health needs. Does not look beyond obvious resources and has a limited understanding of what is available and what they are entitled to. 8. Ability to find good health information (5 items) Is an ‘information explorer’. Actively uses a diverse range of sources to find information and is up to date. Cannot access health information when required. Is dependent on others to offer information. 9. Understanding health information well enough to know what to do (5 items) Can understand all written and spoken information (including numerical information) in relation to their health and write appropriately on forms where required. Has problems understanding any written or spoken health information or instructions about treatments or medications. Unable to read or write well enough to complete medical forms. Adapted from The Ophelia Manual [ 23 ] [insert Table 1 here] The nine HLQ domains are measured through 44 items across nine scales, with four to six items per scale. Scales 1 to 5 (usually described as Part 1) use response options indicating degree of agreement with the item statements on a scale ranging from 1 (Strongly disagree) to 4 (Strongly agree). Scales 6 to 9 (Part 2) have response options indicating degree of difficulty with doing the tasks indicated by the items from 1 (Cannot do or always difficult) to 5 (Always easy). The HLQ scoring procedure is to calculate the nine independent scale scores. There is no single overall score. The score of each scale is calculated by averaging the scores of all items within a scale with equal weighting. Relative higher scores indicate health literacy strengths and relative lower scores reveal areas of potential challenges [ 5 ]. Translation and Cultural adaptation Based on the Translation Integrity Protocol (TIP) [ 24 ], the English version of the HLQ was translated into Hindi through a structured and collaborative process. The TIP, provided by the HLQ developers, is a detail item intent guide, which outlines the meaning, purpose, and appropriate interpretations of each item. An initial version was produced where two formally trained professional translators developed a recommended forward translation. A back translation was then generated by a third translator who was blind to the English version. A consensus meeting was held with the HLQ developer (RHO) to produce a recommended version for testing. To ensure the translated HLQ was suitable for this study, one researcher (RP), with expertise in health and culture and familiarity with both languages, carried out a review of the Hindi version. This version was then further reviewed by a second researcher (MK), who examined the phrasing and cultural appropriateness. In a group consensus meeting, chaired by RHO and included MK and RP, every item was discussed in depth using a group cognitive interview process. The meaning of each translated items was compared with the HLQ intent of each item, as per the TIP requirements, to check for linguistic and cultural appropriateness and, importantly, conceptual and strength equivalence of the concept in each item. The outcomes of interviews were presented to a panel of nine experts for further discussion to finalise the HLQ Hindi version. Data collection Cognitive interviews were conducted in Hindi prior to the cross-sectional survey. During cognitive interviews, participants completed the HLQ using pen and paper format while the interviewer noted any items when any participants seemed to take longer to answer or items that participants had difficulty answering. After they completed the HLQ, the interviewer then explored each item with the participants by asking “What were you thinking when you answered this question?” and “Why did you select that answer?” These questions elicit the ways in which participants engage with and respond to the items, which helps to determine the extent to which they understood each item as intended and described in the TIP [ 24 ]. Each participant was asked specifically if certain items and words were difficult for them to understand or answer. For such items or words, they were asked to suggest better words or sentences. The interview data were reviewed using content analysis [ 25 ] to determine how participants understand and engage with the content of each item and if any of the translated items required revision. For the cross-sectional survey, demographic and health questions, in addition to the HLQ, were also collected. These included age, gender, education, occupation, presence of any chronic illness, attendance at hospital emergency in the past one year, enrolled in government-sponsored health insurance programs, and if assistance was required to complete the survey. Four nursing students were trained over two days to administer the survey following ethical data collection procedures. Participants completed the survey using pen and paper. If the participant could not read or write, the data collector asked the participant the questions and recorded their answers on the paper survey. Statistical analysis Analysis was undertaken using SPSS Version 29.0.2.0 [ 26 ] and Mplus Version 8.11 [ 27 ]. The data were checked for normality, range, extreme values and missing data. For the HLQ, if more than half of the items were missing from a scale, that scale score was not included. Otherwise, the expectation maximization algorithm was used to impute missing values as in other HLQ studies [ 28 – 30 ]. Descriptive statistics were generated for the socio-demographic data and item characteristics, including mean scores, standard deviations (SDs), and floor and ceiling effects. Floor and ceiling effects refer to a high proportion of participants endorsing the minimum and maximum scores, respectively. Presence of ≥ 15% of participants scoring the top and bottom of a scale’s range is considered substantial and indicates possible poor discrimination at the minimum or maximum values [ 31 , 32 ], suggesting an item may be too easy or too difficult, respectively, for survey participants [ 33 ]. As per other HLQ studies [ 5 , 9 , 29 ], item difficulty was determined by the proportion of participants responding to Strongly disagree and Disagree as compared to Agree and Strongly agree for Part 1 scales. For Part 2 scales, difficulty was determined by the proportion of participants answering Cannot do or always difficult, Usually difficult, and Sometimes difficult compared with Usually easy and Always easy. The range of item difficulty within each scale was evaluated to determine if each scale included items demonstrating various levels of difficulty. As intended by the HLQ developers, the range of item difficulty within the scales enables the HLQ to be sensitive to varying degrees of health literacy challenges experienced by communities in different contexts [ 5 ]. As the constructs of the HLQ were specified a priori , CFA was used to evaluate the internal structure of the HLQ Hindi version [ 34 ]. First, a one-factor CFA model was fitted to the data for each scale, followed by the fitting of a nine-factor model, using the WSLMV estimator available in MPlus. The WSLMV estimator was chosen because it does not assume normal distribution of variables, hence, it is the recommended estimator for modelling categorical data [ 35 , 36 ]. This is also a highly restrictive estimation approach, i.e., no correlated residuals or cross-loadings. Standardised factor loadings with 95% confidence interval (CI) and R 2 (variance of observed variable explained by latent variables) of each item were examined. Factor loading, the strength of the association between an observable variable with the latent factor, is generally considered good or acceptable if it is > 0.50 [ 37 , 38 ]. However, to provide a good estimate of acceptable construct reliability, a threshold value of > 0.60 is recommended for scales with three or more items [ 39 ]. To examine model fit, multiple fit indices were used, including the chi-square test, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean-Square Error of Approximation (RMSEA) and Standardised Root Mean Square Residual (SRMR). A non-significant value ( p > 0.05) for the chi-square test indicates good fit. However, the chi-square test assumes multivariate normality of data and is sensitive to sample size. Therefore, a review of multiple fit indices is required to allow for a holistic view of model fit [ 40 ]. Indicative threshold values of ‘close fit’ for the other fit indices are CFI ≥ 0.95, TLI ≥ 0.95, SRMR ≤ 0.08 and RMSEA ≤ 0.06, while a value of ≤ 0.08 for the RMSEA indicates ‘reasonable’ fit [ 41 – 43 ]. Apart from the fit indices, modification indices (MIs) and standardised expected parameter change (SEPC) generated in the Mplus outputs were examined. The MI and SEPC are statistics that can inform model improvements by suggesting that certain fixed parameters should instead be estimated (e.g. correlated residues for one-factor models and cross-loadings for multi-factor models). The MI is a chi-square value associate with fixed parameters. A value of over 3.84, which corresponds with 1 degree of freedom at a p value of 0.05, represents potential improvement by adding or freeing that parameter. The SEPC provides an estimate of the size of the misspecification [ 44 ]. Both statistics, together with careful analysis of theoretical plausibility, are recommended to be used to examine model misspecification, with a large MI combined with a positive value of SEPC > 0.20 indicating a potential misspecification [ 44 , 45 ]. Finally, inter-factor correlations generated from the nine-factor CFA model will inform discriminant validity. A correlation of > 0.80 usually indicates insufficient discriminant validity [ 35 ]. However, a restrictive maximum likelihood estimation approach as in this study tends to lead to highly inflated estimates of inter-factor correlations [ 46 , 47 ], while high inter-factor correlations may suggest the presence of a higher-order factor [ 5 , 48 ]. To measure reliability, both Raykov’s composite reliability and Cronbach’s alpha were used. While Cronbach’s alpha is a popular measure, it is a biased estimate of reliability, especially when items do not have equal factor loadings and item errors may be correlated, as in the case of most tools. Raykov’s composite reliability is explicitly based on a one-factor (‘congeneric’) model that allows for unequal factor loadings, thus allowing the items to be measured with varying degrees of precision. As such, it provides an unbiased reliability estimate if the factor model is an adequate fit to the data and avoids the limitations of Cronbach’s alpha [ 5 , 49 , 50 ]. Nevertheless, Cronbach’s alpha was also calculated for comparison with other previous HLQ studies. Reliability was considered acceptable when Raykov’s composite reliability or Cronbach’s alpha was ≥ 0.70 and 0.95 is an indication of item redundancy and/or an excessive number of items [ 52 , 53 ]. Results Cognitive interviews Half of the 10 participants interviewed were female and homemakers, with the remaining being male with different occupations. The mean age of participants was 37.7 years (SD = 6.9, range: 24 to 46). Half of the participants had undergraduate degrees, while three had completed only primary school. Participants demonstrated a clear understanding of most of the translated items during the cognitive interviews. For example, when responding to Scale 4 ‘Social support for health’ item 4.2 ‘When I feel ill, the people around me really understand what I am going through’, P01 (Participant 01) answered ‘Strongly agree’ and said “Yes, my family is supportive” while P04 who also answered ‘Strongly agree’ indicated that “My spouse understands” . Their responses showed that they could quickly relate the meaning of ‘people around me’ to their personal context of family or spouse. When answering Scale 5 ‘Appraisal of health information’ item 5.2 ‘When I see new information about health, I check up on whether it is true or not’, both P06 and P09 answered ‘Strongly agree’ and commented “I google it” or “I ask madam (healthcare worker)” . Both comments were in line with the item intent. However, six out of 10 participants found it difficult to understand the translated term of ‘healthcare providers’ [स्वास्थ्य सेवा प्रदाता]. When answering Scale 1 ‘Feeling understood and supported by healthcare providers’ item 1.1 ‘I have at least one healthcare provider who knows me well’, P01 said “We only go to the doctor, we don’t meet other healthcare professionals” , P04 asked “Is it doctor?” while P06 asked “Does healthcare provider mean a doctor?” Participants suggested that replacing it with the Hindi word ‘doctor’, which is a commonly used description by people for all types of healthcare professionals. The outcomes of interviews were presented to the expert panel and a decision was taken to replace the word ‘healthcare providers’ [स्वास्थ्य सेवा प्रदाता] with ‘doctor’ [डॉक्टर]. Otherwise, no further revision was required based on the data. While the translated items were mostly understood as intended, the cognitive interviewing data revealed that there is a strong reliance on the social and healthcare networks in supporting people’s different health literacy dimensions in the Indian rural setting. When responding to Scale 5 ‘Appraisal of health information’ item 5.2 ‘When I see new information about health, I check up on whether it is true or not’, some participants indicated that they relied on people around them for health information appraisal. P04 answered ‘Agree’ and indicated that she would ask her husband while P05 (also answered ‘Agree’) said: “My kids know how to use the internet” . When answering Scale 7 ‘Navigating the healthcare system’ item 7.3 ‘Decide which healthcare provider you need to see’, P02 responded ‘Agree’ because “My family or friends tell me where to go” . With item 7.1 ‘Find the right health care’, P04 answered ‘Usually difficulty’ because “I have to ask others to identify the right doctor” . For P03 who answered ‘Usually easy’, he commented “I can just go to the family doctor” , implicating the role of healthcare professionals in helping people navigate the healthcare system. Healthcare professionals were also important in appraising health information and managing health. When answering Scale 5 ‘Appraisal of health information’ item 5.4 ‘I know how to find out if the health information I receive is right or not’, P05 answered ‘Disagree’ with the comment “I have to ask a doctor” while P03 answered ‘Strongly disagree’ because “Doctors would know, how should I know” . P02 indicated that ‘Talking to the doctor is necessary, otherwise managing health is difficult” when commenting on the reason for answering ‘Disagree’ for Scale 3 ‘Actively managing my health’ item 3.1 ‘I spend quite a lot of time actively managing my health’. The role of doctors is also apparent in responses related to Scales 8 ‘Ability to find good health information and 9 ‘Understanding health information well enough to know what to do’. See Table 2 . Table 2 Themes of reliance on social and healthcare networks from cognitive interviewing Theme 1: Reliance on social networks 5. Appraisal of health information P04 “I ask my husband.” P05 “My kids know how to use internet.” 7. Navigating the healthcare system P02 “My family or friends tell me where to go.” P03 “My family help me go there.” P04 “I have to ask other to identify the right doctor.” P05 “I ask my spouse.” P06 “I have to ask others.” Theme 2: Reliance on healthcare networks 3. Actively managing my health P02 “Talking to the doctor is necessary, otherwise managing health is difficult.” 5. Appraisal of health information P03 “Doctors would know, how should I know.” P05 “I have to ask doctor.” P09 “I ask madam (healthcare worker).” 7. Navigating the healthcare system P03 “I just go to the family doctor.” P04 “From the government dispensary.” P08 “Doctor knows what is right, what is wrong.” 8. Ability to find good health information P02 “I do as doctor says.” P07 “Doctors tell us what to do.” P10 “Doctors tell the right information; we do not need to verify that.” 9. Understand health information well enough to know what to do P06 “Why read labels, doctors tell how many times to eat it.” Sociodemographic characteristics of survey participants A total of 260 participants from 223 households completed the survey. Among these, 37 households contributed two participants each, representing 37 groups of participants from the same household. The mean age of participants was 36.9 years (SD 12.3, range: 19 to 72 years). Almost two thirds (61.5%) were in the 18 to 35 years age group. A majority (61.5%) of the participants were women, almost one-third (31.2%) did not complete primary school while 21.1% had university education, demonstrating diversity in educational background. Half of the participants (50%) were doing home duties, 24.6% worked full time, and 15.4% were full-time students (Table 3 ). Chronic conditions reported included back pain (13.1%), hypertension (7.3%), arthritis (6.9%), heart problems (5.0%), diabetes (4.6%), hypothyroidism (4.6%), and asthma (1.5%). Fifteen percent of the participants had been hospitalised in the past one year. Some participants (19.2%) were enrolled in government-sponsored health insurance programs, which provided cashless or subsidised healthcare services. A few participants (5.7%) had private health insurance coverage. Almost half of the survey participants (45.4%) responded to the survey questions by interview. Time to complete the HLQ ranged from 15 to 20 minutes. Table 3 Socio-demographic characteristics of survey participants (n = 260) Socio-demographic characteristics n % Age (in years) (mean: 36.9 years, SD: 12.3, range: 19–72) 18–29 85 32.7 30–39 75 28.8 40–49 56 21.5 50–59 26 10.0 60–69 15 5.8 70–79 3 1.1 Gender Female 160 61.5 Male 100 38.5 Education Below primary school 81 31.2 Primary to less than high school (completed Grade 5 or below) 41 15.8 High school (completed Grade 10) 64 24.6 Higher secondary school (completed Grade 12) 19 7.3 Undergraduate degree(s) 49 18.8 Postgraduate degree(s) 6 2.3 Occupation Full time 64 24.6 Part time 18 6.9 Home duties 130 50.0 Full-time student 40 15.4 Retired 6 2.3 Presence of chronic illness (can have more than one) Arthritis 18 6.9 Asthma 4 1.5 Back pain 34 13.1 Diabetes 12 4.6 Heart problems 13 5.0 Hypertension 19 7.3 Hypothyroidism 12 4.6 Attended emergency department of a hospital in the past one year 39 15.0 Enrolled in government-sponsored health insurance programs 50 19.2 Have private health insurance 15 5.8 Required assistance in completing survey 118 45.4 Floor and ceiling effects There were no missing data for the HLQ items. Ceiling effects were found in items in two of the Part 1 scales. There were four items of Scale 4 ‘Social support for health’ where 17.7% to 23.5% participants endorsed the highest response option (4 = Strongly agree), indicating a high proportion of people in this setting tended to have good social support. For Scale 5 ‘Appraisal of health information’, 15.0% to 16.2% of participants scored three items out of five items at the highest level (Strongly agree). No floor effect was found for Part 1 items. For Part 2 scales, two items demonstrated marginal ceiling effects, with one item (16.9%) from Scale 7 ‘Navigating the healthcare systems’ and one item (17.7%) from Scale 9 ‘Understanding health information well enough to know what to do’. These two scales also had one item each (21.5% for Scale 7 and 30.4% for Scale 9) that demonstrated floor effects. See Table 4 . Table 4 Psychometric properties of the Health Literacy Questionnaire (HLQ) in Hindu Scales and Items Mean (SD) Floor effect* (%) Ceiling effect* (%) Difficulty^ (95% CI) Factor loading (95% CI) R 2 Part 1 Scales (Score range: 1 to 4) 1. Feeling understood and supported by healthcare providers 1.1 I have at least one healthcare provider who… 2.55 (0.87) 13.5 11.9 0.43 (0.37–0.49) 0.62 (0.52–0.73) 0.39 1.2 I have at least one healthcare provider I can… 2.67 (0.77) 6.5 12.3 0.38 (0.33–0.44) 0.77 (0.69–0.85) 0.59 1.3 I have the healthcare providers I need… 2.63 (0.83) 10.0 12.7 0.39 (0.33–0.45) 0.65 (0.56–0.75) 0.43 1.4 I can rely on at least one… 2.71 (0.79) 8.1 12.7 0.34 (0.28–0.40) 0.70 (0.62–0.80) 0.50 Model fit: χ 2 WLSMV (2) = 2.427, p = 0.297, CFI = 0.999, TLI = 0.997, RMSEA = 0.029 (90% CI: 0.000-0.130), SRMR = 0.015 Raykov’s composite reliability: 0.74 (0.68–0.79), Cronbach’s alpha: 0.74 2. Having sufficient information to manage my health 2.1 I feel I have good information about health… 2.61 (0.87) 14.6 11.5 0.36 (0.30–0.42) 0.63 (0.54–0.72) 0.40 2.2 I have enough information to help me deal… 2.52 (0.76) 9.2 7.3 0.46 (0.40–0.52) 0.70 (0.61–0.79) 0.49 2.3 I am sure I have all the information I… 2.48 (0.82) 13.5 7.7 0.47 (0.40–0.53) 0.77 (0.69–0.86) 0.60 2.4 I have all the information I need to 2.49 (0.80) 10.4 8.8 0.49 (0.43–0.55) 0.72 (0.63–0.81) 0.52 Model fit: χ 2 WLSMV (2) = 0.506, p = 0.777, CFI = 1.000, TLI = 1.000, RMSEA = 0.000 (90% CI: 0.000-0.081), SRMR = 0.006 Raykov’s composite reliability: 0.75 (0.70–0.80), Cronbach’s alpha: 0.75 3. Actively managing my health 3.1 I spend quite a lot of time actively managing… 2.63 (0.81) 8.5 12.7 0.41 (0.35–0.47) 0.56 (0.45–0.68) 0.32 3.2 I make plans for what I need to do to be… 2.65 (0.83) 10.0 12.7 0.37 (0.31–0.43) 0.62 (0.52–0.72) 0.38 3.3 Despite other things in my life, I make time… 2.64 (0.81) 8.8 11.9 0.39 (0.33–0.45) 0.42 (0.29–0.55) 0.18 3.4 I set my own goals about health and fitness 2.54 (0.82) 10.8 10.0 0.45 (0.39–0.51) 0.65 (0.55–0.75) 0.42 3.5 There are things that I do regularly… 2.64 (0.77) 6.5 11.2 0.41 (0.35–0.47) 0.83 (0.74–0.92) 0.69 Fit with 2 correlated residuals: χ 2 WLSMV (3) = 4.281, p = 0.233, CFI = 0.997, TLI = 0.991, RMSEA = 0.041 (90% CI: 0.000-0.119), SRMR = 0.015 Raykov’s composite reliability: 0.74 (0.69–0.79), Cronbach’s alpha: 0.74 4. Social Support for health 4.1 I can get access to several people who… 2.56 (0.88) 13.8 12.3 0.42 (0.36–0.48) 0.61 (0.51–0.70) 0.37 4.2 When I feel ill, the people around me really… 2.85 (0.77) 5.4 18.1 0.28 (0.22–0.33) 0.60 (0.50–0.71) 0.37 4.3 If I need help, I have plenty of people I… 2.79 (0.82) 6.9 18.1 0.32 (0.27–0.38) 0.83 (0.76–0.90) 0.69 4.4 I have at least one person… 2.78 (0.82) 7.3 17.7 0.33 (0.27–0.38) 0.58 (0.48–0.67) 0.33 4.5 I have strong support from… 2.90 (0.84) 7.7 23.5 0.25 (0.20–0.31) 0.68 (0.59–0.77) 0.46 Model fit: χ 2 WLSMV (5) = 10.137, p = 0.071, CFI = 0.990, TLI = 0.980, RMSEA = 0.063 (90% CI: 0.000-0.119), SRMR = 0.023 Raykov’s composite reliability: 0.75 (0.70–0.79), Cronbach’s alpha: 0.74 5. Appraisal of health information 5.1 I compare health information from different 2.58 (0.90) 11.5 16.2 0.47 (0.40–0.53) 0.70 (0.62–0.78) 0.49 5.2 When I see new information about health, I… 2.66 (0.83) 8.8 14.2 0.39 (0.33–0.45) 0.65 (0.57–0.73) 0.42 5.3 I always compare health information from… 2.57 (0.80) 9.6 9.6 0.43 (0.37–0.49) 0.71 (0.64–0.78) 0.51 5.4 I know how to find out if the health… 2.54 (0.89) 12.3 15.0 0.48 (0.42–0.55) 0.72 (0.65–0.79) 0.52 5.5 I ask healthcare providers about the quality… 2.70 (0.83) 8.1 15.4 0.37 (0.31–0.43) 0.73 (0.66–0.79) 0.54 Model fit: χ 2 WLSMV (5) = 12.108, p = 0.033, CFI = 0.991, TLI = 0.981, RMSEA = 0.074 (90% CI: 0.019–0.128), SRMR = 0.023 Raykov’s composite reliability: 0.79 (0.75–0.83), Cronbach’s alpha: 0.79 Part 2 Scales (Score range: 1 to 5) 6. Ability to actively engage with healthcare providers 6.1 Make sure that healthcare providers understand… 2.89 (1.03) 9.2 4.6 0.70 (0.65–0.76) 0.78 (0.72–0.84) 0.61 6.2 Feel able to discuss your health concerns with a… 3.29 (1.05) 4.2 11.9 0.54 (0.48–0.60) 0.81 (0.76–0.87) 0.66 6.3 Have good discussions about your health… 3.22 (1.08) 4.6 13.5 0.60 (0.54–0.66) 0.81 (0.76–0.85) 0.65 6.4 Discuss things with healthcare providers… 2.91 (1.08) 9.2 6.5 0.69 (0.63–0.75) 0.75 (0.69–0.81) 0.56 6.5 Ask healthcare providers questions to get… 3.14 (1.07) 5.4 10.8 0.62 (0.56–0.67) 0.80 (0.75–0.86) 0.65 Model fit: χ 2 WLSMV (5) = 20.175, p = 0.001, CFI = 0.992, TLI = 0.984, RMSEA = 0.108 (90% CI: 0.062–0.159), SRMR = 0.022 Raykov’s composite reliability: 0.87 (0.84–0.90), Cronbach’s alpha: 0.87 7. Navigating the healthcare system 7.1 Find the right healthcare 2.72 (1.22) 21.5 5.0 0.67 (0.62–0.73) 0.79 (0.74–0.85) 0.63 7.2 Get to see the healthcare providers I need to 3.03 (1.03) 5.4 8.1 0.67 (0.61–0.73) 0.76 (0.70–0.82) 0.58 7.3 Decide which healthcare provider you need… 3.06 (1.00) 5.4 6.2 0.65 (0.59–0.70) 0.85 (0.81–0.89) 0.72 7.4 Make sure you find the right place to get… 3.22 (1.21) 8.1 16.9 0.56 (0.50–0.62) 0.85 (0.81–0.89) 0.72 7.5 Find out what healthcare services you are… 2.80 (1.07) 11.9 5.8 0.74 (0.69–0.80) 0.75 (0.69–0.81) 0.56 7.6 Work out what is the best care for you 2.97 (1.10) 8.8 8.8 0.68 (0.62–0.74) 0.77 (0.71–0.82) 0.59 Model fit: χ 2 WLSMV (9) = 13.015, p = 0.162, CFI = 0.999, TLI = 0.998, RMSEA = 0.041 (90% CI: 0.000-0.087), SRMR = 0.014 Raykov’s composite reliability: 0.90 (0.88–0.92), Cronbach’s alpha: 0.89 8. Ability to find good health information 8.1 Find information about health problems 2.92 (1.12) 10.4 6.2 0.64 (0.58–0.70) 0.77 (0.71–0.83) 0.60 8.2 Find health information from several… 2.97 (1.05) 9.2 5.8 0.67 (0.62–0.73) 0.67 (0.60–0.74) 0.45 8.3 Get information about health so you are… 3.10 (1.05) 6.5 6.9 0.60 (0.54–0.66) 0.79 (0.73–0.85) 0.62 8.4 Get health information in words you… 3.06 (1.20) 11.2 12.7 0.62 (0.56–0.68) 0.80 (0.74–0.85) 0.63 8.5 Get health information by yourself 2.90 (1.04) 8.1 5.8 0.70 (0.65–0.76) 0.77 (0.71–0.83) 0.59 Model fit: χ 2 WLSMV (5) = 6.881, p = 0.230, CFI = 0.999, TLI = 0.997, RMSEA = 0.038 (90% CI: 0.000-0.100), SRMR = 0.011 Raykov’s composite reliability: 0.85 (0.82–0.88), Cronbach’s alpha: 0.85 9. Understanding health information well enough to know what to do 9.1 Confidently fill medical forms in the correct… 3.11 (1.14) 10.4 11.9 0.63 (0.57–0.69) 0.74 (0.67–0.80) 0.54 9.2 Accurately follow the instructions from… 3.14 (1.03) 6.2 9.6 0.65 (0.59–0.71) 0.71 (0.64–0.79) 0.51 9.3 Read and understand written health… 3.23 (1.21) 8.5 17.7 0.56 (0.50–0.62) 0.85 (0.81–0.90) 0.73 9.4 Read and understand all the information on… 2.66 (1.35) 30.4 8.1 0.67 (0.61–0.72) 0.86 (0.81–0.91) 0.74 9.5 Understand what healthcare providers are… 3.16 (1.02) 4.6 8.5 0.60 (0.54–0.66) 0.76 (0.70–0.81) 0.57 Model fit: χ 2 WLSMV (5) = 11.429, p = 0.044, CFI = 0.996, TLI = 0.993, RMSEA = 0.070 (90% CI: 0.011–0.125), SRMR = 0.016 Raykov’s composite reliability: 0.87 (0.85–0.90), Cronbach’s alpha: 0.86 *Floor or ceiling effect: ≥ 15.0% of participants endorsed the highest response option (Strongly agree/Always easy) or lowest response option (Strongly disagree/Cannot do or always difficult) respectively. Items with floor or ceiling effects are in bold. ^Difficulty is defined as the proportion of participants responding to Strongly disagree and Disagree for Scales 1 to 5. For Scales 6 to 9, it was calculated by the proportion of participants answering Cannot do or always difficult and Usually difficulty and Sometimes difficult. Notes: CI: Confidence interval; χ 2 WLSMV : Chi-square test using weighted least squares mean and variance adjusted estimator; CFI: Comparative Fit Index; TLI: Tucker–Lewis index; RMSEA: root mean square error of approximation; SRMR: standardized root mean square residual. Items are truncated. Contact the HLQ developers for the full items. [insert Table 4 here] Item difficulty For Scales 1 to 5, Scale 3. ‘Actively managing my health’ had the smallest range of difficulty (37% to 45%), with item 3.4 ‘I set my own goals about health and fitness’ being the most difficult (45% answered Strongly disagree or Disagree). Scale 4. ‘Social support for health’ had the widest range of difficulty (25% to 42%) while this scale could also be considered the ‘easiest scale’ as it was the only scale with two items that had difficulty levels below 30%. With Part 2 scales, Scale 8 ‘Ability to find good health information’ had items with the smallest difficulty range (60% to 70%), while the widest difficulty range was observed for Scale 7 ‘Navigating the healthcare system’ (56% to 74%). The difficulty levels for items in Part 2 were all above 50% (i.e., more than 50% answered Cannot do or always difficult or Usually difficult or Sometimes difficult). Scale 7 item 7.5 ‘Find out what healthcare services you are entitled to’ had the highest difficulty level of 74% among all Part 2 items (Table 4 ). Confirmatory factor analysis For the one-factor models, model fit was satisfactory to reasonable with no large MIs with SEPC > 0.20 except for Scale 3 ‘Actively managing my health’. For the initial fit, the chi-square test results indicated satisfactory fit (p > 0.05) for five scales (Scales 1, 2, 4, 7 and 8) while CFI and TLI suggested satisfactory fit (> 0.95) for all scales except Scale 3. The RMSEA suggested satisfactory fit ( 0.20 identified. A modified model including two correlated residuals (item 3.3 ‘Despite other things in my life, I make time to be healthy’ with 3.1 ‘I spend quite a lot of time actively managing my health’ with correlated residual = 0.34; and item 3.3 ‘Despite other things in my life, I make time to be healthy’ with 3.2 ‘I make plans for what I need to do to be healthy’ with correlated residual = 0.23) resulted in excellent fit (Table 4 ). Factor loadings for the one-factor models were above 0.60 except for two items of Scale 3 and one item for Scale 4. Item 3.3 ‘Despite other things in my life, I make time to be healthy’ had a loading of 0.42 after model adjustment. The other two items, 3.1 ‘I spend quite a lot of time actively managing my health from Scale 3 (factor loading = 0.56) and 4.4 ‘I have at least one person who can come to medical appointments with me’ from Scale 4 (factor loading = 0.58) were both above 0.50 (Table 4 ). The nine-factor restrictive model fitted the data well: χ 2 WLSMV (866) = 1371.788, p = 0.000, CFI = 0.970, TLI = 0.967, RMSEA = 0.047 (90% CI: 0.043–0.052), and SRMR = 0.053. Yet, some moderately large MIs with SEPC > 0.20 were observed, indicating potential cross-loadings, including item 7.1 ‘Find the right healthcare’ from scale 7 with Scale 1 ‘Feeling understood and supported by healthcare providers’ (MI = 25.62, SEPC = 0.37). An adjusted model was fitted to the data, but no model improvement was observed. While this adjusted model resulted in less MIs with SEPC > 0.20, there was still one large MI with SEPC > 0.20. A second model was tested by adding a cross-loading of item 5.5 ‘I ask healthcare providers about the quality of health information I find’ of Scale 5 with Scale 6 ‘Ability to actively engage with healthcare providers’ (MI = 15.53, SEPC = 0.34). Again, the model could not be improved. Therefore, it was decided that the original model was adequate. Factor loadings of all items in the nine-factor model were above 0.60 except for three items (two items from Scale 4 and one item from Scale 5) but were all above 0.50. See Table 5 . Table 5 Factor loadings of the nine-factor model of the Health Literacy Questionnaire (HLQ) in Hindi Model fit: χ 2 WLSMV (866) = 1371.788, p = 0.000, CFI = 0.970, TLI = 0.967, RMSEA = 0.047 (90% CI: 0.043–0.052), SRMR = 0.053 Item 1. Feeling understood 2. Sufficient information 3. Actively managing 4. Social support 5. Appraisal 6. Actively engage 7. Navigate 8. Find good information 9. Understand information 1.1 0.77 1.2 0.68 1.3 0.72 1.4 0.63 2.1 0.77 2.2 0.64 2.3 0.71 2.4 0.71 3.1 0.69 3.2 0.61 3.3 0.62 3.4 0.63 3.5 0.69 4.1 0.71 4.2 0.57 4.3 0.73 4.4 0.59 4.5 0.70 5.1 0.70 5.2 0.59 5.3 0.69 5.4 0.71 5.5 0.79 6.1 0.78 6.2 0.82 6.3 0.77 6.4 0.76 6.5 0.83 7.1 0.85 7.2 0.77 7.3 0.82 7.4 0.81 7.5 0.74 7.6 0.81 8.1 0.78 8.2 0.69 8.3 0.77 8.4 0.79 8.5 0.76 9.1 0.73 9.2 0.73 9.3 0.85 9.4 0.80 9.5 0.81 [insert Table 5 here] Discriminant validity Based on the nine-factor model, inter-factor correlations ranged from 0.70 (Scales 1 and 9) to 1.04 (Scales 7 and 8). Out of the 36 pairs of correlations, eight pairs (Scales 2 and 3, 2 and 5, 6 and 7, 6 and 8, 6 and 9, 7 and 8, 7 and 9, and 8 and 9) were > 0.95 while 15 pairs from both Part 1 and Part 2 scales were > 0.80, indicating insufficient discriminant validity and possible presence of higher-order factor(s) among some of the scales. See Table 6 . Table 6 Inter-factor correlations of the Health Literacy Questionnaire (HLQ) scales based on nine-factor confirmatory factor analysis Scale 1. Feeling understood 2. Sufficient information 3. Actively managing 4. Social support 5. Appraisal 6. Actively engage 7. Navigate 8. Find good information 2. Sufficient information 0.95 3. Actively managing 0.82 0.97 4. Social support 0.88 0.82 0.89 5. Appraisal 0.90 0.96 0.93 0.81 6. Actively engage 0.75 0.80 0.82 0.82 0.75 7. Navigate 0.73 0.85 0.83 0.78 0.74 0.97 8. Find good information 0.76 0.85 0.83 0.76 0.82 0.97 1.04 9. Understand information 0.70 0.78 0.80 0.73 0.76 0.98 0.99 1.02 Note: Inter-factor correlations > 0.80 are in italics and underlined, while > 0.95 are in bold. Reliability As shown in Table 4 , Raykov’s composite reliability of Scales 1 to 5 ranged from 0.74 (Scales 1 and 3) to 0.79 (Scale 5), demonstrating acceptable reliability. For Scales 6 to 9, the results ranged from 0.85 (Scale 8) to 0.90 (Scale 7), indicating good reliability. Results of Cronbach’s alpha were similar to that of Raykov’s composite reliability. Discussion This study applied a rigorous process to translate and culturally adapt the HLQ into Hindi for application in rural India. The results show that the Hindi version of the HLQ demonstrates strong psychometric properties in a setting that is vastly different from the Australian setting where the tool was originally developed. The solid validity evidence implicates that the HLQ Hindi version will be a valuable tool to gain insights into the health literacy strengths and challenges of people living in the resource-poor rural India setting with diverse demographics. It can be used to identify interventions to support their health literacy development. The cognitive interviews found that the translated version was generally well-understood. Only one translated term, ‘healthcare providers’, caused some confusion and was revised to a general Hindi term for ‘doctors’. People in the rural and peri-urban areas in India commonly refer to anyone who gives them medicines as ‘doctors’ – this includes qualified medical officers, chemists, paramedical workers and even informal providers. Primary Health Centres, which are the main and often nearest source of care, usually have only one or two medical officers, while services like physiotherapy or dietetics are rarely available in government services. As a result, the term ‘healthcare providers’ was unfamiliar, and participants related more easily to the word ‘doctors’. As such, the selected Hindi term of ‘doctors’ was regarded as the most appropriate equivalent to the English. It is worth noting that such a substitution may not be necessary when applying the HLQ in urban areas, where the community’s knowledge of healthcare roles is likely to be more distinct. As expected in a marginalised rural community, the observed ceiling effects in the HLQ are small. Ceiling effects were identified in Scale 4 ‘Social support for health’, indicating that for many participants, getting social support is easy for participants to achieve. This ceiling effect likely reflects the communal and family-oriented culture in rural India, where individuals often live in joint families and maintain close ties with neighbours. This social structure ensures that people usually have someone to accompany them to medical appointments or help them when needed, aligning with the supportive behaviours measured in this HLQ scale. Similar findings were observed in our previous study in another village of Chandigarh [ 54 ], where participants also reported high levels of social support for health. Surprisingly, some marginal ceiling effects were observed for Scale 5 ‘Appraisal of health information’. The sample did include people who were well educated, with 21.1% of participants had undergraduate or postgraduate degrees. For example, in the cognitive interview, a college graduate showed confidence in all Scale 5 items and mentioned using google to verify health information. Another plausible explanation could be that, in this rural context, people relied on trusted local sources (e.g., families, doctors or health workers) rather than independently appraising diverse or conflicting health information, thus perceiving themselves as competent without encountering much uncertainty. For example, participants of the cognitive interviews indicated that they would ask their families or doctors when responding to items in Scale 5. It is also possible that the issue of misinformation from the internet is less prominent in this resource-poor rural setting. On the other hand, no scales exhibited floor effects for Part 1 scales. For Part 2 scales, two items – 7.1 ‘Find the right healthcare’ of Scale 7 (21.5%) and 9.4 ‘Read and understand all the information on medication labels’ of Scale 9 (30.4%) – were highly endorsed at the lowest end (Cannot do or always difficult). The floor effect in both items may be attributed to low literacy as 31% of participants had below primary school education. Besides, there was also the high proportion of women (65%) in the survey sample. Women often face inequitable access to care and dependency on males to access healthcare services in India [ 55 ]. This was also reflected in the cognitive interviews when women tended to mention asking family or spouse in their responses. While ceiling and floor effects were found for some items, the results of item difficulty indicated that each scale has items representing a range of difficulty, enabling participants to express their level of health literacy across all constructs. While Scale 5 ‘Appraisal of health information’ of the Australian English HLQ had the most difficult items in Part 1 [ 5 ], Scales 2, 3 and 5 of the Hindi version all had three items with item difficulty level above 40%. Besides, item difficulty levels of the Hindi version are all much higher than the Australian English version, especially for the items in Part 2, reflecting the limited resources in the rural setting and low education of most participants. Using a restrictive estimation approach, the nine one-factor models of the HLQ Hindi version demonstrate excellent to acceptable fit except for Scale 3 ‘Actively managing my health’. Based on the MIs, two correlated residuals were tested in a modified model. The pattern of a cluster of three items for Scale 3 suggest that there may be conceptual overlap among these items that is independent of the primary construct. Clearly, these items do capture distinct aspects about managing health: item 3.3 highlights goal setting as a motivator for self-care behaviours; item 3.1 reflects ongoing effort in managing health; and item 3.2 emphasizes structured planning for health management. The correlated residuals may be caused by some common conceptions related to the terms used for time and planning in Hindi language, such as ‘make time’, ‘spend quite a lot of time’ or ‘make plans’. While the modifications do lead to an excellent fit, future qualitative and quantitative validity testing of the Hindi version of the HLQ may help identify possible wording improvement or if this conceptual overlap leads to construct imprecision. Overall, factor loadings of the one-factor models are > 0.60 except for three items, of which two items were close to the threshold of 0.6, indicating strong association between the items and their relevant constructs. The only item with a low loading is item 3.3 (0.42), which is a result of model adjustment. An important finding was that the data fitted well to a highly restrictive nine-factor model. While we identified some large MIs which indicate the potential of cross-loadings in the model results, modifications did not lead to any model improvement or lowering of the inter-factor correlations. Future validity testing may consider using Bayesian Structural Equation Modeling to further explore potential cross loadings [ 9 , 56 ]. The high inter-factor correlations were as expected given the use of a highly restrictive nine-factor model. Most HLQ validity studies including the original Australian English version [ 5 , 7 – 10 , 57 ] have also found insufficient discriminant validity among the Part 2 scales. This led to the suggestion by Elsworth et al that there is possibly a higher-order factor of personal agency and efficacy, particularly among the items that constitute the Part 2 scales [ 48 ]. However, for this study, higher inter-factor correlations were also observed between Part 1 scales. This suggests that there may be another higher-order factor present or a general factor running through all items in this particular setting. As indicated in the cognitive interview data, there is a strong reliance on the social and healthcare networks even when participants responded to items related to finding (Scale 8), understanding (Scale 9) and appraising (Scale 5) health information, navigating the healthcare system (Scale 7) or managing their health (Scale 3). The results illustrate how health is often seen as a collective effort. In rural areas, where resources may be limited and health literacy is variable, individuals may rely heavily on family or friends or healthcare providers for guidance, creating a sense of shared responsibility in managing health. This communal efficacy where individuals depend on others for health-related tasks, may lead to high correlation between the HLQ scales. Using a Bayesian approach for the multifactor CFA in future validity testing may provide more insights into the inter-factor correlations when the potential cross-loadings (and correlated residuals) are accounted for [ 9 , 56 ]. While high inter-factor correlations may indicate construct overlap in some context, another study using the HLQ in the same region found that the nine scales tend to be associated with different exogenous variables such as age, education, socio-economic class or having chronic illness. Further exploration of the data using cluster analysis identified eight clusters of people with varying higher or lower scores across the nine scales [ 54 ]. These results provide substantial support for the discriminant validity of HLQ scales. Further studies to explore the presence of high inter-factor correlations, using other discriminant validity testing methods, such as the Fornell and Larcker method [ 58 ] or the more recent Ronkko and Cho method [ 46 ], are recommended to gain deeper insights into this unique setting. Strengths and limitations This study followed a rigorous procedure to evaluate the psychometric properties of the HLQ in Hindi. By integrating qualitative and quantitative data, we also demonstrated how validity evidence can be systematically developed for a health measure. For data collection, we incorporated an interview-based method for participants who were unable to complete the questionnaire. As such, people with limited reading and writing skills, which amounted to close to half of the survey participants, were included in the survey. However, the study also had some limitations. While Hindi is spoken and understood in the northern regions of India, the Hindi version of the HLQ may not be as applicable in other regions. The selection of the Chandigarh Union Territory of India was based on the fact that there was already a good rapport between the community and the researchers. While the goodwill could support data collection, it may also lead to social desirability bias. However, given the mean scale scores are generally lower (below 3.00 indicating ‘Agree’ for items of Part 1 scales and below 4.00 indicating ‘Usually easy’ for items of Part 2 scales, see Table 4 ), the effect of social desirability seemed to be limited. There is also the potential of clustering effect given our sampling method, despite efforts to ensure participants were interviewed separately. Besides, we conducted the study among the general population where participants were comparatively healthier than people recruited from a healthcare facility. Also, the sample was younger (61.5% below the age of 40 years) than the general population. Therefore, the HLQ data should be interpreted with caution when it is used to assess the health literacy needs of populations with other socio-demographic and health profiles. Conclusion The HLQ was found to have strong psychometric properties when it was first developed and in versions later adapted and translated for different settings. However, to use it in the Indian rural setting with confidence, validity testing is required to ensure the data collected can be interpreted and used to make meaningful and equitable public health decisions in the Indian rural context. This study showed that the Hindi HLQ possesses the same strong internal structure and reliability as the Australian English HLQ. The findings of this study indicate that the Hindi HLQ can be used in a Hindi speaking community in northern India to assess health literacy strengths and challenges. It will help inform the development of public health interventions to improve health and equity. Abbreviations WHO World Health Organization HLQ Health Literacy Questionnaire CFA Confirmatory factor analysis (CFA) PGIMER Post-Graduate Institute of Medical Education and Research WLSMV Weighted least squares mean and variance adjusted MPHW Medical Officer and Multi-Purpose Health Worker TIP Translation Integrity Protocol SD standard deviation CI confidence interval CFI Comparative Fit Index TLI Tucker-Lewis Index RMSEA Root Mean-Square Error of Approximation SRMR Standardised Root Mean Square Residual MI modification indices SEPC standardised expected parameter change Declarations Ethics approval and consent to participate The study was approved by the Institute Ethics Committee, Post-Graduate Institute of Medical Education and Research Chandigarh (INT/IEC/2019/000414). It was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments. Before participation in the cognitive interview or the survey, informed consent was obtained from all participants after briefing them about the study purpose and that participation was voluntary. Consent for publication Not applicable. Availability of data and materials The dataset generated and analysed are not publicly available due to the privacy of survey participants but are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Indian Council of Medical Research Fellowship to RP; and a National Health and Medical Research Council Investigator Grant [2025522 to CC, MH and RHO]. Author Contributions Funding acquisition: RP, RHO; study conceptualisation: MK, RP, RHO; data curation: RP; Methodology: MK, MG, SK, RK, RP, CC, GE, RHO; data analysis and interpretation of results: CC, GE, MK, RK, RP, MH, RHO; manuscript draft and preparation: RP, CC, MK, RK All authors reviewed and approved the final version of this manuscript. Acknowledgements We would like to thank the Medical Officer and Multi-Purpose Health Workers of the Civil Dispensary in the selected village for their support to access the households. We are grateful to the nursing students who helped us in data collection. Most of all, we would like to thank the respondents who participated in the cognitive interviews and cross-sectional survey. References Parker RM, Baker DW, Williams MV, Nurss JR. The test of functional health literacy in adults: a new instrument for measuring patients' literacy skills. J Gen Intern Med. 1995;10:537–41. https://doi.org/10.1007/BF02640361 . Rowlands G, Khazaezadeh N, Oteng-Ntim E, Seed P, Barr S, Weiss BD. Development and validation of a measure of health literacy in the UK: the newest vital sign. BMC Public Health. 2013;13:1–9. https://doi.org/10.1186/1471-2458-13-116 . WHO. Health literacy development for the prevention and control of noncommunicable diseases:Volume 2. A globally relevant perspective. Licence: CC BY- NC- SA 3.0 IGO. Geneva: World Health Organization.; 2022 [cited 2025 June 17]. Available from: https://www.who.int/publications/i/item/9789240055339 WHO. Health literacy Geneva: World Health Organization; 2022 [cited 2025 April 15]. Available from: https://www.who.int/news-room/fact-sheets/detail/health-literacy Osborne RH, Batterham RW, Elsworth GR, Hawkins M, Buchbinder R. The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ). BMC Public Health. 2013;13:1–17. https://doi.org/10.1186/1471-2458-13-658 . Wahl A, Hermansen Å, Osborne RH, Larsen MH. A validation study of the Norwegian version of the Health Literacy Questionnaire: a robust nine-dimension factor model. journalssagepubcom. 2020;49:471–8. https://doi.org/10.1177/1403494820926428 . Debussche X, Lenclume V, Balcou-Debussche M, Alakian D, Sokolowsky C, Ballet D, et al. Characterisation of health literacy strengths and weaknesses among people at metabolic and cardiovascular risk: Validity testing of the Health Literacy Questionnaire. SAGE open Med. 2018;6:2050312118801250. https://doi.org/10.1177/2050312118801250 . Nolte S, Osborne RH, Dwinger S, Elsworth GR, Conrad ML, Rose M, et al. German translation, cultural adaptation, and validation of the Health Literacy Questionnaire (HLQ). PLoS ONE. 2017;12:1–12. https://doi.org/10.1371/journal.pone.0172340 . Maindal HT, Kayser L, Norgaard O, Bo A, Elsworth GR, Osborne RH. Cultural adaptation and validation of the Health Literacy Questionnaire (HLQ): robust nine-dimension Danish language confirmatory factor model. Springerplus. 2016;5:1232. https://doi.org/10.1186/s40064-016-2887-9 . Kolarcik P, Cepova E, Madarasova Geckova A, Elsworth GR, Batterham RW, Osborne RH. Structural properties and psychometric improvements of the Health Literacy Questionnaire in a Slovak population. Int J Public Health 2017. 2017;62:5. https://doi.org/10.1007/S00038-017-0945-X . Park JH, Osborne RH, Kim HJ, Bae SH. Cultural and linguistic adaption and testing of the Health Literacy Questionnaire (HLQ) among healthy people in Korea. PLoS ONE. 2022;17:e0271549. https://doi.org/10.1371/journal.pone.0271549 . Ha Dinh TT, Bonner A. Psychometric properties of the health literacy questionnaire tested in Vietnamese adults with chronic diseases. BMC Public Health. 2025;25:44. https://doi.org/10.1186/s12889-024-21156-7 . Moraes KL, Brasil VV, Mialhe FL, De Carvalho Sampaio HA, Sousa ALL, Canhestro MR et al. Validation of the Health Literacy Questionnaire (HLQ) to brazilian portuguese. Acta Paulista de Enfermagem: Escola Paulista de Enfermagem, Universidade Federal de São Paulo; 2021. https://doi:10.37689/ACTA-APE/2021AO02171 Boateng MA, Agyei-Baffour P, Angel S, Enemark U. Translation, cultural adaptation and psychometric properties of the Ghanaian language (Akan; Asante Twi) version of the Health Literacy Questionnaire. BMC Health Serv Res: BioMed Central Ltd; 2020. pp. 1–15. https://doi:10.1186/S12913-020-05932-W/TABLES/4 Osborne RH, Cheng CC, Nolte S, Elmer S, Besancon S, Budhathoki SS, et al. Health literacy measurement: embracing diversity in a strengths-based approach to promote health and equity, and avoid epistemic injustice. BMJ Global Health. 2022;7:e009623. https://doi.org/10.1136/bmjgh-2022-009623 . American Educational Research Association, American Psychological Association. National Council on Measurement in Education. Standards for educational and psychological testing. Washington (DC): American Educational Research Association; 2014. DeWalt DA, Rothrock N, Yount S, Stone AA. Evaluation of item candidates: the PROMIS qualitative item review. Med Care. 2007;45. https://doi.org/10.1097/01.mlr.0000254567.79743.e2 . :S12-21. Beatty PC, Willis GB. Research synthesis: The practice of cognitive interviewing. Pub Opin Q. 2007;71:287–311. https://doi.org/10.1093/poq/nfm006 . Leslie CJ, Hawkins M, Smith DL. Using the health literacy questionnaire (HLQ) with providers in the early intervention setting: a qualitative validity testing study. Int J Environ Res Public Health. 2020;17:2603. https://doi.org/10.3390/ijerph17072603 . Liang X, Yang Y. An evaluation of WLSMV and Bayesian methods for confirmatory factor analysis with categorical indicators. Int J Quant Res Educ. 2014;2:17–38. https://doi.org/10.1504/IJQRE.2014.060972 . Moshagen M, Musch J. Sample size requirements of the robust weighted least squares estimator. Methodology. 2014. https://doi.org/10.1027/1614-2241/a000068 . Zhang T. Relative Performance of MLR, WLSMV, and Bayes Estimators: An Investigation Using Confirmatory Factor Analysis with Ordinal Data. University of South Carolina; 2024. Osborne RH, Elsworth ES, Hawkins M, Cheng C. The Ophelia Manual: The Optimising Health Literacy and Access (Ophelia) process to plan and implement National Health Literacy Demonstration Projects. Melbourne, Australia: Centre for Global Health and Equity, School of Health Sciences, Swinburne University of Technology; 2021. Hawkins M, Cheng C, Elsworth GR, Osborne RH. Translation method is validity evidence for construct equivalence: analysis of secondary data routinely collected during translations of the Health Literacy Questionnaire (HLQ). 2020. https://doi.org/10.1186/s12874-020-00962-8 Drisko JW, Maschi T. Content analysis: Oxford University Press; 2016. IBM Corp. IBM SPSS Statistics for Windows, Version 29.0.2.0. Armonk. NY: IBM Corp.; 2023. Muthén L, Muthén B. &. Mplus User’s Guide, Eighth Edi. ed. Muthén 2017. Beauchamp A, Buchbinder R, Dodson S, Batterham RW, Elsworth GR, McPhee C, et al. Distribution of health literacy strengths and weaknesses across socio-demographic groups: a cross-sectional survey using the Health Literacy Questionnaire (HLQ). BMC Public Health. 2015;15:678. https://doi.org/10.1186/s12889-015-2056-z . Do ÓDN, Goes AR, Elsworth G, Raposo JF, Loureiro I, Osborne RH. Cultural Adaptation and Validity Testing of the Portuguese Version of the Health Literacy Questionnaire (HLQ). Int J Environ Res Public Health. 2022;19:6465. https://doi.org/10.3390/ijerph19116465 . Nolte S, Osborne RH, Dwinger S, Elsworth GR, Conrad ML, Rose M, et al. German translation, cultural adaptation, and validation of the Health Literacy Questionnaire (HLQ). PLoS ONE. 2017;12:e0172340. https://doi.org/10.1371/journal.pone.0172340 . Fayers PM, Machin D. Quality of Life: The Assessment, Analysis and Interpretation of Patient-reported Outcomes. Wiley; 2013. Driban JB, Morgan N, Price LL, Cook KF, Wang C. Patient-Reported Outcomes Measurement Information System (PROMIS) instruments among individuals with symptomatic knee osteoarthritis: a cross-sectional study of floor/ceiling effects and construct validity. BMC Musculoskelet Disord. 2015;16:253. https://doi.org/10.1186/s12891-015-0715-y . Garin O. Ceiling Effect. Michalos AC. editor. Dordrecht: Springer; 2014. Schreiber JB, Nora A, Stage FK, Barlow EA, King J. Reporting structural equation modeling and confirmatory factor analysis results: A review. J educational Res. 2006;99. https://doi.org/10.3200/JOER.99.6.323-338 . :323 – 38. Brown TA. Confirmatory factor analysis for applied research. Guilford; 2015. Finney SJ, DiStefano C. Non-normal and categorical data in structural equation modeling. Struct equation modeling: second course. 2006;10:269–314. Tabachnick BG, Fidell LS, Ullman JB. Using multivariate statistics: pearson Boston, MA; 2007. Hair JF Jr, Anderson RE, Tatham RL, Black WC. Multivariate data analysis with readings. Prentice-Hall, Inc.; 1995. Dominguez-Lara S. Proposal for cut-offs for factor loadings: A construct reliability perspective. Enferm Clin (Engl Ed). 2018;28:401–2. https://doi.org/10.1016/j.enfcli.2018.06.002 . Alavi M, Visentin DC, Thapa DK, Hunt GE, Watson R, Cleary M. Chi-square for model fit in confirmatory factor analysis. J Adv Nurs. 2020;76:2209–11. https://doi.org/10.1111/jan.14399 . Browne MW, Cudeck R. Alternative ways of assessing model fit. Sociol methods Res. 1992;21:230–58. https://doi.org/10.1177/0049124192021002005 . Yu CY. Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes. University of California, Los Angeles; 2002. West SG, Taylor AB, Wu W. Model fit and model selection in structural equation modeling. Handb Struct equation Model. 2012;1:209–31. Whittaker TA. Using the modification index and standardized expected parameter change for model modification. J Experimental Educ. 2012;80:26–44. https://doi.org/10.1080/00220973.2010.531299 . Saris WE, Satorra A, Van der Veld WM. Testing structural equation models or detection of misspecifications? Structural equation modeling 2009;16:561 – 82. https://doi.org/10.1080/10705510903203433 Rönkkö M, Cho E. An updated guideline for assessing discriminant validity. Organizational Res methods. 2022;25:6–14. https://doi.org/10.1177/1094428120968614 . Marsh HW, Morin AJ, Parker PD, Kaur G. Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis. Ann Rev Clin Psychol. 2014;10:85–110. https://doi.org/10.1146/annurev-clinpsy-032813-153700 . Elsworth GR, Nolte S, Cheng C, Hawkins M, Osborne RH. Modelling variance in the multidimensional Health Literacy Questionnaire: Does a General Health Literacy factor account for observed interscale correlations? SAGE Open Med. 2022;10:20503121221124771. https://doi.org/10.1177/20503121221124771 . Raykov T. Scale construction and development using structural equation modeling. 2012. Bell SM, Chalmers RP, Flora DB. The impact of measurement model misspecification on coefficient omega estimates of composite reliability. Educ Psychol Meas. 2024;84:5–39. https://doi.org/10.1177/00131644231155804 . Sarstedt M, Ringle CM, Hair JF. Partial least squares structural equation modeling. Handbook of market research. Springer; 2021. pp. 587–632. Hair JF Jr, Hult GTM, Ringle CM, Sarstedt M, Danks NP, Ray S et al. Evaluation of reflective measurement models. Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. 2021:75–90. Boyle GJ. Does item homogeneity indicate internal consistency or item redundancy in psychometric scales? Personality and in dividual differences 1991;12:291–4. https://doi.org/10.1016/0191-8869(91)90115-R Passi R, Kaur M, Lakshmi P, Cheng C, Hawkins M, Osborne RH. Health literacy strengths and challenges among residents of a resource-poor village in rural India: Epidemiological and cluster analyses. PLOS Global Public Health. 2023;3:e0001595. https://doi.org/10.1371/journal.pgph.0001595 . Singh S, Rajak B, Dehury RK, Mathur S, Samal A. Differential access of healthcare services and its impact on women in India: A systematic literature review. SN Social Sci. 2023;3:16. https://doi.org/10.1007/s43545-023-00607-9 . Elsworth GR, Beauchamp A, Osborne RH. Measuring health literacy in community agencies: a Bayesian study of the factor structure and measurement invariance of the health literacy questionnaire (HLQ). BMC Health Serv Res. 2016;16:508. https://doi.org/10.1186/s12913-016-1754-2 . Rademakers J, Waverijn G, Rijken M, Osborne R, Heijmans M. Towards a comprehensive, person-centred assessment of health literacy: translation, cultural adaptation and psychometric test of the Dutch Health Literacy Questionnaire. BMC Public Health. 2020;20:1–12. https://doi.org/10.1186/s12889-020-09963-0 . Fornell C, Larcker DF. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J Mark Res. 1981;18:39–50. https://doi.org/10.1177/002224378101800104 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Dec, 2025 Editor assigned by journal 16 Dec, 2025 Submission checks completed at journal 16 Dec, 2025 First submitted to journal 11 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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The concept of health literacy has progressed from measurement of reading, comprehension and numeracy skills [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] to people\u0026rsquo;s ability and resources to access, understand, appraise, remember and use health information in ways that maintain good health and well-being [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The World Health Organization (WHO) recognizes that health literacy incudes how people build knowledge and competencies to manage their health through everyday experiences, social connections, and interactions with health services [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As such, health literacy is not only related to an individual\u0026rsquo;s abilities but also reflects the ability and responsiveness of health services to ensure equitable access to, and use of health services [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe nine-dimension Health Literacy Questionnaire (HLQ) was developed using a grounded approach to derive health literacy concepts directly from the lived experience of health service users and a diverse range of Australian and international health practitioners in 2012 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The HLQ is now being used in more than 90 countries and has been translated and adapted into 47 language versions with validity testing studies undertaken in Europe [\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], Asia [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], South America [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and Africa [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMeasuring health literacy can provide insights into people\u0026rsquo;s lived experiences, strengths, and challenges as they attempt to use health information and navigate health systems that are shaped by local norms, entitlements, and practices [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Given this complexity, applying measures developed in other settings without testing whether people interpret and respond to items as intended, risks producing misleading data and overlooking the knowledge and capabilities of communities [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, validity testing is necessary to ensure that interpretations of scores from a health literacy measure are conceptually and culturally appropriate for a context, such as India, and that actions and decisions based on score interpretations are contextually meaningful and equitable [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In a country where Hindi is the most widely spoken language, adapting and testing the Hindi version of the HLQ using qualitative and quantitative methods supports measurement that is locally grounded, promotes equity, and avoids the ethical risk of epistemic injustice [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The validity evidence will determine if the Hindi HLQ can accurately capture health literacy strengths and challenges in the Indian rural setting to inform the design of locally relevant health interventions.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eThe present study aimed to culturally adapt and examine the validity evidence on response process, internal structure and reliability of the Hindi version of the HLQ among adults living in a village of northern India. This study was a mixed-method study, involving two phases. In the first phase, we translated and culturally adapted the HLQ into Hindi and conducted cognitive interviews to investigate the degree to which the participants interpreted and responded to the items as intended by the questionnaire developers (response process evidence). For the second phase, we undertook a cross-sectional survey to evaluate the tool\u0026rsquo;s internal structure, including floor and ceiling effects, item difficulty, dimensionality based on confirmatory factor analysis (CFA), and discriminant validity, and reliability.\u003c/p\u003e \u003cp\u003eSetting and participants\u003c/p\u003e \u003cp\u003eA village from the Chandigarh Union Territory of India was selected purposively for this study. The village has been a training area for students from the National Institute of Nursing Education, Post-Graduate Institute of Medical Education and Research (PGIMER), Chandigarh. As part of their community health training, nursing students regularly visit the village to provide health education, basic care, and support to local families. This existing relationship helped the researcher (RP) engage with the community more effectively and facilitate smooth data collection.\u003c/p\u003e \u003cp\u003eParticipants for this study were aged 18 years and above, with no upper age limit. We included participants who could speak and understand Hindi. Since we did not assess literacy formally, we used an interview-based approach if participants had difficulty reading the questionnaire. For the cognitive interviewing, we selected a sample size of 10 based on previous HLQ cognitive testing studies and the literature, which have shown this number to be sufficient for identifying major issues with item interpretation and response [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For the testing of CFA using the weighted least squares mean and variance adjusted (WSLMV) estimator for categorical data as in this study, sample size recommendations may range from 200 to above [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Considering the size and accessibility of the population at the data collection site, a sample size of 260 was estimated.\u003c/p\u003e \u003cp\u003eParticipant recruitment\u003c/p\u003e \u003cp\u003eIn India, every village is served by a primary healthcare facility staffed by key healthcare providers including a Medical Officer and Multi-Purpose Health Worker(s) (MPHW). These personnel maintain regular contact with the community and are familiar with the village layout. To design the recruitment procedure, we consulted the Medical Officer and MPHW of the Civil Dispensary in the selected village to help identify the village\u0026rsquo;s sub-areas and support access to households. The study village had eight sub-areas. Out of the eight sub-areas, houses were numbered in only six sub-areas, and the numbered houses were selected for the study. In the six selected sub-areas, there were a total of 2,024 houses. A proportionate random sampling technique was used to select houses in each sub-area. Using computer-generated random numbers, we selected the required number of houses from each sub-area. The sampled houses were approached according to their serial order in the sampling list.\u003c/p\u003e \u003cp\u003eFor the cognitive interviews, one adult participant was selected from each of 10 randomly selected households within these six sub-areas. For the cross-sectional survey, assuming that two adults lived in each house, a total of 130 houses were selected to reach the sample size of 260 adults. In households with more than one eligible adult, participants were interviewed separately by different interviewers, or each participant was provided with a questionnaire to complete separately to minimize clustering effect, i.e., influence on each other\u0026rsquo;s responses. All adults from the sampled houses were recruited in the survey until the required sample size of 260 was reached. In cases where a house was found locked or residents were unavailable over two consecutive visits from researchers, the house on the right side of the sampled house was selected for inclusion.\u003c/p\u003e \u003cp\u003eThe Health Literacy Questionnaire (HLQ)\u003c/p\u003e \u003cp\u003eA license to use and adapt the HLQ into Hindi was obtained by PGIMER from the HLQ authors (L1777IA). The HLQ consists of nine health literacy domains [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for the HLQ scales and descriptions for the interpretation of higher and lower scores [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Health Literacy Questionnaire (HLQ) scales with descriptions of higher and lower scores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher HLQ score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower HLQ score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePart 1: Scales 1 to 5\u003c/p\u003e \u003cp\u003eScore range: 1 to 4 (1\u0026thinsp;=\u0026thinsp;Strongly disagree, 2\u0026thinsp;=\u0026thinsp;Disagree, 3\u0026thinsp;=\u0026thinsp;Agree, 4\u0026thinsp;=\u0026thinsp;Strongly agree)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1. Feeling understood and supported by healthcare providers\u003c/b\u003e (4 items)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHas an established relationship with at least one healthcare provider who knows them well. The person trusts this provider to give useful advice and information and to assist them to understand information and make decisions about their health.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIs unable to engage with doctors and other healthcare providers. The person doesn\u0026rsquo;t have a regular healthcare provider and/or has difficulty trusting healthcare providers as a source of information and/or advice.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2. Having sufficient information to manage my health\u003c/b\u003e (4 items)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeels confident that they have all the information that they need to live with and manage their condition and to make decisions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeels that there are many gaps in their knowledge and that they don\u0026rsquo;t have the information they need to live with and manage their health concerns.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3. Actively managing my health\u003c/b\u003e (5 items)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecognises the importance of and can take responsibility for their health. The person proactively engages in their care and makes their own decisions about their health.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoesn\u0026rsquo;t see their health as their responsibility. The person is not engaged in their health care and regards health care as something that is done to them.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4. Social support for health\u003c/b\u003e (5 items)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe person\u0026rsquo;s social system provides them with all the support they want or need.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompletely alone and unsupported.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5. Appraisal of health information\u003c/b\u003e (5 items)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAble to identify good information and reliable sources of information. They can resolve conflicting information by themselves or with help from others.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo matter how hard they try, the person cannot understand most health information and becomes confused when there is conflicting information.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePart 2: Scales 6 to 9\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eScore range: 1 to 5 (1\u0026thinsp;=\u0026thinsp;Cannot do or always difficult, 2\u0026thinsp;=\u0026thinsp;Usually difficult, 3\u0026thinsp;=\u0026thinsp;Sometimes difficult, 4\u0026thinsp;=\u0026thinsp;Usually easy, 5\u0026thinsp;=\u0026thinsp;Always easy)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6. Ability to actively engage with healthcare providers\u003c/b\u003e (5 items)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIs proactive about their health and feels in control in relationships with healthcare providers. Is able to seek advice from additional healthcare providers when necessary. Keeps going until they get what they want. Empowered.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIs passive in their approach to health care, inactive, does not seek or clarify information and advice and/or service options. Accepts information without question. Unable to ask questions to get information or to clarify what they don\u0026rsquo;t understand. Accepts what is offered without seeking to ensure it meets their needs. Feels unable to share concerns.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7. Navigating the healthcare system\u003c/b\u003e (6 items)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAble to find out about services and supports so they get all their needs met. Able to advocate on their own behalf at the system and service level.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnable to advocate on their own behalf and unable to find someone who can help them use the healthcare system to address their health needs. Does not look beyond obvious resources and has a limited understanding of what is available and what they are entitled to.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8. Ability to find good health information\u003c/b\u003e (5 items)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIs an \u0026lsquo;information explorer\u0026rsquo;. Actively uses a diverse range of sources to find information and is up to date.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCannot access health information when required. Is dependent on others to offer information.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9. Understanding health information well enough to know what to do\u003c/b\u003e (5 items)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCan understand all written and spoken information (including numerical information) in relation to their health and write appropriately on forms where required.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHas problems understanding any written or spoken health information or instructions about treatments or medications. Unable to read or write well enough to complete medical forms.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAdapted from The Ophelia Manual [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eThe nine HLQ domains are measured through 44 items across nine scales, with four to six items per scale. Scales 1 to 5 (usually described as Part 1) use response options indicating degree of agreement with the item statements on a scale ranging from 1 (Strongly disagree) to 4 (Strongly agree). Scales 6 to 9 (Part 2) have response options indicating degree of difficulty with doing the tasks indicated by the items from 1 (Cannot do or always difficult) to 5 (Always easy). The HLQ scoring procedure is to calculate the nine independent scale scores. There is no single overall score. The score of each scale is calculated by averaging the scores of all items within a scale with equal weighting. Relative higher scores indicate health literacy strengths and relative lower scores reveal areas of potential challenges [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTranslation and Cultural adaptation\u003c/p\u003e \u003cp\u003eBased on the Translation Integrity Protocol (TIP) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], the English version of the HLQ was translated into Hindi through a structured and collaborative process. The TIP, provided by the HLQ developers, is a detail item intent guide, which outlines the meaning, purpose, and appropriate interpretations of each item. An initial version was produced where two formally trained professional translators developed a recommended forward translation. A back translation was then generated by a third translator who was blind to the English version. A consensus meeting was held with the HLQ developer (RHO) to produce a recommended version for testing.\u003c/p\u003e \u003cp\u003e To ensure the translated HLQ was suitable for this study, one researcher (RP), with expertise in health and culture and familiarity with both languages, carried out a review of the Hindi version. This version was then further reviewed by a second researcher (MK), who examined the phrasing and cultural appropriateness. In a group consensus meeting, chaired by RHO and included MK and RP, every item was discussed in depth using a group cognitive interview process. The meaning of each translated items was compared with the HLQ intent of each item, as per the TIP requirements, to check for linguistic and cultural appropriateness and, importantly, conceptual and strength equivalence of the concept in each item. The outcomes of interviews were presented to a panel of nine experts for further discussion to finalise the HLQ Hindi version.\u003c/p\u003e \u003cp\u003eData collection\u003c/p\u003e \u003cp\u003eCognitive interviews were conducted in Hindi prior to the cross-sectional survey. During cognitive interviews, participants completed the HLQ using pen and paper format while the interviewer noted any items when any participants seemed to take longer to answer or items that participants had difficulty answering. After they completed the HLQ, the interviewer then explored each item with the participants by asking \u0026ldquo;What were you thinking when you answered this question?\u0026rdquo; and \u0026ldquo;Why did you select that answer?\u0026rdquo; These questions elicit the ways in which participants engage with and respond to the items, which helps to determine the extent to which they understood each item as intended and described in the TIP [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Each participant was asked specifically if certain items and words were difficult for them to understand or answer. For such items or words, they were asked to suggest better words or sentences. The interview data were reviewed using content analysis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] to determine how participants understand and engage with the content of each item and if any of the translated items required revision.\u003c/p\u003e \u003cp\u003eFor the cross-sectional survey, demographic and health questions, in addition to the HLQ, were also collected. These included age, gender, education, occupation, presence of any chronic illness, attendance at hospital emergency in the past one year, enrolled in government-sponsored health insurance programs, and if assistance was required to complete the survey.\u003c/p\u003e \u003cp\u003eFour nursing students were trained over two days to administer the survey following ethical data collection procedures. Participants completed the survey using pen and paper. If the participant could not read or write, the data collector asked the participant the questions and recorded their answers on the paper survey.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAnalysis was undertaken using SPSS Version 29.0.2.0 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and Mplus Version 8.11 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The data were checked for normality, range, extreme values and missing data. For the HLQ, if more than half of the items were missing from a scale, that scale score was not included. Otherwise, the expectation maximization algorithm was used to impute missing values as in other HLQ studies [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Descriptive statistics were generated for the socio-demographic data and item characteristics, including mean scores, standard deviations (SDs), and floor and ceiling effects. Floor and ceiling effects refer to a high proportion of participants endorsing the minimum and maximum scores, respectively. Presence of \u0026ge;\u0026thinsp;15% of participants scoring the top and bottom of a scale\u0026rsquo;s range is considered substantial and indicates possible poor discrimination at the minimum or maximum values [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], suggesting an item may be too easy or too difficult, respectively, for survey participants [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. As per other HLQ studies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], item difficulty was determined by the proportion of participants responding to Strongly disagree and Disagree as compared to Agree and Strongly agree for Part 1 scales. For Part 2 scales, difficulty was determined by the proportion of participants answering Cannot do or always difficult, Usually difficult, and Sometimes difficult compared with Usually easy and Always easy. The range of item difficulty within each scale was evaluated to determine if each scale included items demonstrating various levels of difficulty. As intended by the HLQ developers, the range of item difficulty within the scales enables the HLQ to be sensitive to varying degrees of health literacy challenges experienced by communities in different contexts [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs the constructs of the HLQ were specified \u003cem\u003ea priori\u003c/em\u003e, CFA was used to evaluate the internal structure of the HLQ Hindi version [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. First, a one-factor CFA model was fitted to the data for each scale, followed by the fitting of a nine-factor model, using the WSLMV estimator available in MPlus. The WSLMV estimator was chosen because it does not assume normal distribution of variables, hence, it is the recommended estimator for modelling categorical data [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This is also a highly restrictive estimation approach, i.e., no correlated residuals or cross-loadings. Standardised factor loadings with 95% confidence interval (CI) and R\u003csup\u003e2\u003c/sup\u003e (variance of observed variable explained by latent variables) of each item were examined. Factor loading, the strength of the association between an observable variable with the latent factor, is generally considered good or acceptable if it is \u0026gt;\u0026thinsp;0.50 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, to provide a good estimate of acceptable construct reliability, a threshold value of \u0026gt;\u0026thinsp;0.60 is recommended for scales with three or more items [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo examine model fit, multiple fit indices were used, including the chi-square test, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean-Square Error of Approximation (RMSEA) and Standardised Root Mean Square Residual (SRMR). A non-significant value (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) for the chi-square test indicates good fit. However, the chi-square test assumes multivariate normality of data and is sensitive to sample size. Therefore, a review of multiple fit indices is required to allow for a holistic view of model fit [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Indicative threshold values of \u0026lsquo;close fit\u0026rsquo; for the other fit indices are CFI\u0026thinsp;\u0026ge;\u0026thinsp;0.95, TLI\u0026thinsp;\u0026ge;\u0026thinsp;0.95, SRMR\u0026thinsp;\u0026le;\u0026thinsp;0.08 and RMSEA\u0026thinsp;\u0026le;\u0026thinsp;0.06, while a value of \u0026le;\u0026thinsp;0.08 for the RMSEA indicates \u0026lsquo;reasonable\u0026rsquo; fit [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Apart from the fit indices, modification indices (MIs) and standardised expected parameter change (SEPC) generated in the Mplus outputs were examined. The MI and SEPC are statistics that can inform model improvements by suggesting that certain fixed parameters should instead be estimated (e.g. correlated residues for one-factor models and cross-loadings for multi-factor models). The MI is a chi-square value associate with fixed parameters. A value of over 3.84, which corresponds with 1 degree of freedom at a \u003cem\u003ep\u003c/em\u003e value of 0.05, represents potential improvement by adding or freeing that parameter. The SEPC provides an estimate of the size of the misspecification [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Both statistics, together with careful analysis of theoretical plausibility, are recommended to be used to examine model misspecification, with a large MI combined with a positive value of SEPC\u0026thinsp;\u0026gt;\u0026thinsp;0.20 indicating a potential misspecification [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Finally, inter-factor correlations generated from the nine-factor CFA model will inform discriminant validity. A correlation of \u0026gt;\u0026thinsp;0.80 usually indicates insufficient discriminant validity [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, a restrictive maximum likelihood estimation approach as in this study tends to lead to highly inflated estimates of inter-factor correlations [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], while high inter-factor correlations may suggest the presence of a higher-order factor [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo measure reliability, both Raykov\u0026rsquo;s composite reliability and Cronbach\u0026rsquo;s alpha were used. While Cronbach\u0026rsquo;s alpha is a popular measure, it is a biased estimate of reliability, especially when items do not have equal factor loadings and item errors may be correlated, as in the case of most tools. Raykov\u0026rsquo;s composite reliability is explicitly based on a one-factor (\u0026lsquo;congeneric\u0026rsquo;) model that allows for unequal factor loadings, thus allowing the items to be measured with varying degrees of precision. As such, it provides an unbiased reliability estimate if the factor model is an adequate fit to the data and avoids the limitations of Cronbach\u0026rsquo;s alpha [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Nevertheless, Cronbach\u0026rsquo;s alpha was also calculated for comparison with other previous HLQ studies. Reliability was considered acceptable when Raykov\u0026rsquo;s composite reliability or Cronbach\u0026rsquo;s alpha was \u0026ge;\u0026thinsp;0.70 and \u0026lt;\u0026thinsp;0.95 [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. A reliability\u0026thinsp;\u0026gt;\u0026thinsp;0.95 is an indication of item redundancy and/or an excessive number of items [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eCognitive interviews\u003c/p\u003e \u003cp\u003eHalf of the 10 participants interviewed were female and homemakers, with the remaining being male with different occupations. The mean age of participants was 37.7 years (SD\u0026thinsp;=\u0026thinsp;6.9, range: 24 to 46). Half of the participants had undergraduate degrees, while three had completed only primary school.\u003c/p\u003e \u003cp\u003eParticipants demonstrated a clear understanding of most of the translated items during the cognitive interviews. For example, when responding to Scale 4 \u0026lsquo;Social support for health\u0026rsquo; item 4.2 \u0026lsquo;When I feel ill, the people around me really understand what I am going through\u0026rsquo;, P01 (Participant 01) answered \u0026lsquo;Strongly agree\u0026rsquo; and said \u003cem\u003e\u0026ldquo;Yes, my family is supportive\u0026rdquo;\u003c/em\u003e while P04 who also answered \u0026lsquo;Strongly agree\u0026rsquo; indicated that \u003cem\u003e\u0026ldquo;My spouse understands\u0026rdquo;\u003c/em\u003e. Their responses showed that they could quickly relate the meaning of \u0026lsquo;people around me\u0026rsquo; to their personal context of family or spouse. When answering Scale 5 \u0026lsquo;Appraisal of health information\u0026rsquo; item 5.2 \u0026lsquo;When I see new information about health, I check up on whether it is true or not\u0026rsquo;, both P06 and P09 answered \u0026lsquo;Strongly agree\u0026rsquo; and commented \u003cem\u003e\u0026ldquo;I google it\u0026rdquo;\u003c/em\u003e or \u003cem\u003e\u0026ldquo;I ask madam (healthcare worker)\u0026rdquo;\u003c/em\u003e. Both comments were in line with the item intent. However, six out of 10 participants found it difficult to understand the translated term of \u0026lsquo;healthcare providers\u0026rsquo; [स्वास्थ्य सेवा प्रदाता]. When answering Scale 1 \u0026lsquo;Feeling understood and supported by healthcare providers\u0026rsquo; item 1.1 \u0026lsquo;I have at least one healthcare provider who knows me well\u0026rsquo;, P01 said \u003cem\u003e\u0026ldquo;We only go to the doctor, we don\u0026rsquo;t meet other healthcare professionals\u0026rdquo;\u003c/em\u003e, P04 asked \u003cem\u003e\u0026ldquo;Is it doctor?\u0026rdquo;\u003c/em\u003e while P06 asked \u003cem\u003e\u0026ldquo;Does healthcare provider mean a doctor?\u0026rdquo;\u003c/em\u003e Participants suggested that replacing it with the Hindi word \u0026lsquo;doctor\u0026rsquo;, which is a commonly used description by people for all types of healthcare professionals. The outcomes of interviews were presented to the expert panel and a decision was taken to replace the word \u0026lsquo;healthcare providers\u0026rsquo; [स्वास्थ्य सेवा प्रदाता] with \u0026lsquo;doctor\u0026rsquo; [डॉक्टर]. Otherwise, no further revision was required based on the data.\u003c/p\u003e \u003cp\u003eWhile the translated items were mostly understood as intended, the cognitive interviewing data revealed that there is a strong reliance on the social and healthcare networks in supporting people\u0026rsquo;s different health literacy dimensions in the Indian rural setting. When responding to Scale 5 \u0026lsquo;Appraisal of health information\u0026rsquo; item 5.2 \u0026lsquo;When I see new information about health, I check up on whether it is true or not\u0026rsquo;, some participants indicated that they relied on people around them for health information appraisal. P04 answered \u0026lsquo;Agree\u0026rsquo; and indicated that she would ask her husband while P05 (also answered \u0026lsquo;Agree\u0026rsquo;) said: \u003cem\u003e\u0026ldquo;My kids know how to use the internet\u0026rdquo;\u003c/em\u003e. When answering Scale 7 \u0026lsquo;Navigating the healthcare system\u0026rsquo; item 7.3 \u0026lsquo;Decide which healthcare provider you need to see\u0026rsquo;, P02 responded \u0026lsquo;Agree\u0026rsquo; because \u003cem\u003e\u0026ldquo;My family or friends tell me where to go\u0026rdquo;\u003c/em\u003e. With item 7.1 \u0026lsquo;Find the right health care\u0026rsquo;, P04 answered \u0026lsquo;Usually difficulty\u0026rsquo; because \u003cem\u003e\u0026ldquo;I have to ask others to identify the right doctor\u0026rdquo;\u003c/em\u003e. For P03 who answered \u0026lsquo;Usually easy\u0026rsquo;, he commented \u003cem\u003e\u0026ldquo;I can just go to the family doctor\u0026rdquo;\u003c/em\u003e, implicating the role of healthcare professionals in helping people navigate the healthcare system. Healthcare professionals were also important in appraising health information and managing health. When answering Scale 5 \u0026lsquo;Appraisal of health information\u0026rsquo; item 5.4 \u0026lsquo;I know how to find out if the health information I receive is right or not\u0026rsquo;, P05 answered \u0026lsquo;Disagree\u0026rsquo; with the comment \u003cem\u003e\u0026ldquo;I have to ask a doctor\u0026rdquo;\u003c/em\u003e while P03 answered \u0026lsquo;Strongly disagree\u0026rsquo; because \u003cem\u003e\u0026ldquo;Doctors would know, how should I know\u0026rdquo;\u003c/em\u003e. P02 indicated that \u003cem\u003e\u0026lsquo;Talking to the doctor is necessary, otherwise managing health is difficult\u0026rdquo;\u003c/em\u003e when commenting on the reason for answering \u0026lsquo;Disagree\u0026rsquo; for Scale 3 \u0026lsquo;Actively managing my health\u0026rsquo; item 3.1 \u0026lsquo;I spend quite a lot of time actively managing my health\u0026rsquo;. The role of doctors is also apparent in responses related to Scales 8 \u0026lsquo;Ability to find good health information and 9 \u0026lsquo;Understanding health information well enough to know what to do\u0026rsquo;. See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThemes of reliance on social and healthcare networks from cognitive interviewing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTheme 1: Reliance on social networks\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e5. Appraisal of health information\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;I ask my husband.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;My kids know how to use internet.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e7. Navigating the healthcare system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;My family or friends tell me where to go.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;My family help me go there.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;I have to ask other to identify the right doctor.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;I ask my spouse.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;I have to ask others.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTheme 2: Reliance on healthcare networks\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e3. Actively managing my health\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;Talking to the doctor is necessary, otherwise managing health is difficult.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e5. Appraisal of health information\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;Doctors would know, how should I know.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;I have to ask doctor.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;I ask madam (healthcare worker).\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e7. Navigating the healthcare system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;I just go to the family doctor.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;From the government dispensary.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;Doctor knows what is right, what is wrong.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e8. Ability to find good health information\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;I do as doctor says.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;Doctors tell us what to do.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;Doctors tell the right information; we do not need to verify that.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e9. Understand health information well enough to know what to do\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;Why read labels, doctors tell how many times to eat it.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSociodemographic characteristics of survey participants\u003c/p\u003e \u003cp\u003eA total of 260 participants from 223 households completed the survey. Among these, 37 households contributed two participants each, representing 37 groups of participants from the same household. The mean age of participants was 36.9 years (SD 12.3, range: 19 to 72 years). Almost two thirds (61.5%) were in the 18 to 35 years age group. A majority (61.5%) of the participants were women, almost one-third (31.2%) did not complete primary school while 21.1% had university education, demonstrating diversity in educational background. Half of the participants (50%) were doing home duties, 24.6% worked full time, and 15.4% were full-time students (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChronic conditions reported included back pain (13.1%), hypertension (7.3%), arthritis (6.9%), heart problems (5.0%), diabetes (4.6%), hypothyroidism (4.6%), and asthma (1.5%). Fifteen percent of the participants had been hospitalised in the past one year. Some participants (19.2%) were enrolled in government-sponsored health insurance programs, which provided cashless or subsidised healthcare services. A few participants (5.7%) had private health insurance coverage. Almost half of the survey participants (45.4%) responded to the survey questions by interview. Time to complete the HLQ ranged from 15 to 20 minutes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of survey participants (n\u0026thinsp;=\u0026thinsp;260)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocio-demographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (in years) (mean: 36.9 years, SD: 12.3, range: 19\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow primary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary to less than high school (completed Grade 5 or below)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school (completed Grade 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher secondary school (completed Grade 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUndergraduate degree(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostgraduate degree(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePart time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHome duties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull-time student\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePresence of chronic illness (can have more than one)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBack pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeart problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAttended emergency department of a hospital in the past one year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEnrolled in government-sponsored health insurance programs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHave private health insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRequired assistance in completing survey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFloor and ceiling effects\u003c/p\u003e \u003cp\u003eThere were no missing data for the HLQ items. Ceiling effects were found in items in two of the Part 1 scales. There were four items of Scale 4 \u0026lsquo;Social support for health\u0026rsquo; where 17.7% to 23.5% participants endorsed the highest response option (4\u0026thinsp;=\u0026thinsp;Strongly agree), indicating a high proportion of people in this setting tended to have good social support. For Scale 5 \u0026lsquo;Appraisal of health information\u0026rsquo;, 15.0% to 16.2% of participants scored three items out of five items at the highest level (Strongly agree). No floor effect was found for Part 1 items.\u003c/p\u003e \u003cp\u003eFor Part 2 scales, two items demonstrated marginal ceiling effects, with one item (16.9%) from Scale 7 \u0026lsquo;Navigating the healthcare systems\u0026rsquo; and one item (17.7%) from Scale 9 \u0026lsquo;Understanding health information well enough to know what to do\u0026rsquo;. These two scales also had one item each (21.5% for Scale 7 and 30.4% for Scale 9) that demonstrated floor effects. See Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePsychometric properties of the Health Literacy Questionnaire (HLQ) in Hindu\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eScales and Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFloor effect* (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCeiling effect* (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDifficulty^\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFactor loading (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePart 1 Scales (Score range: 1 to 4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e1. Feeling understood and supported by healthcare providers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI have at least one healthcare provider who\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.55 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43 (0.37\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.62 (0.52\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI have at least one healthcare provider I can\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.67 (0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38 (0.33\u0026ndash;0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77 (0.69\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI have the healthcare providers I need\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.63 (0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39 (0.33\u0026ndash;0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65 (0.56\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI can rely on at least one\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.71 (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34 (0.28\u0026ndash;0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70 (0.62\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eModel fit: χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eWLSMV\u003c/sub\u003e(2)\u0026thinsp;=\u0026thinsp;2.427, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.297, CFI\u0026thinsp;=\u0026thinsp;0.999, TLI\u0026thinsp;=\u0026thinsp;0.997, RMSEA\u0026thinsp;=\u0026thinsp;0.029 (90% CI: 0.000-0.130), SRMR\u0026thinsp;=\u0026thinsp;0.015\u003c/p\u003e \u003cp\u003eRaykov\u0026rsquo;s composite reliability: 0.74 (0.68\u0026ndash;0.79), Cronbach\u0026rsquo;s alpha: 0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2. Having sufficient information to manage my health\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI feel I have good information about health\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.61 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36 (0.30\u0026ndash;0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.63 (0.54\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI have enough information to help me deal\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.52 (0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.46 (0.40\u0026ndash;0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70 (0.61\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI am sure I have all the information I\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.48 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47 (0.40\u0026ndash;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77 (0.69\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI have all the information I need to\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.49 (0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49 (0.43\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72 (0.63\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eModel fit: χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eWLSMV\u003c/sub\u003e(2)\u0026thinsp;=\u0026thinsp;0.506, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.777, CFI\u0026thinsp;=\u0026thinsp;1.000, TLI\u0026thinsp;=\u0026thinsp;1.000, RMSEA\u0026thinsp;=\u0026thinsp;0.000 (90% CI: 0.000-0.081), SRMR\u0026thinsp;=\u0026thinsp;0.006\u003c/p\u003e \u003cp\u003eRaykov\u0026rsquo;s composite reliability: 0.75 (0.70\u0026ndash;0.80), Cronbach\u0026rsquo;s alpha: 0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3. Actively managing my health\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI spend quite a lot of time actively managing\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.63 (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41 (0.35\u0026ndash;0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.56 (0.45\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI make plans for what I need to do to be\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.65 (0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37 (0.31\u0026ndash;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.62 (0.52\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDespite other things in my life, I make time\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.64 (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39 (0.33\u0026ndash;0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.42 (0.29\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI set my own goals about health and fitness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.54 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45 (0.39\u0026ndash;0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65 (0.55\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThere are things that I do regularly\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.64 (0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41 (0.35\u0026ndash;0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.83 (0.74\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eFit with 2 correlated residuals: χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eWLSMV\u003c/sub\u003e(3)\u0026thinsp;=\u0026thinsp;4.281, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.233, CFI\u0026thinsp;=\u0026thinsp;0.997, TLI\u0026thinsp;=\u0026thinsp;0.991, RMSEA\u0026thinsp;=\u0026thinsp;0.041 (90% CI: 0.000-0.119), SRMR\u0026thinsp;=\u0026thinsp;0.015\u003c/p\u003e \u003cp\u003eRaykov\u0026rsquo;s composite reliability: 0.74 (0.69\u0026ndash;0.79), Cronbach\u0026rsquo;s alpha: 0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4. Social Support for health\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI can get access to several people who\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.56 (0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42 (0.36\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61 (0.51\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhen I feel ill, the people around me really\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.85 (0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e18.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28 (0.22\u0026ndash;0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60 (0.50\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIf I need help, I have plenty of people I\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.79 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e18.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32 (0.27\u0026ndash;0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.83 (0.76\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI have at least one person\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.78 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e17.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33 (0.27\u0026ndash;0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58 (0.48\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI have strong support from\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.90 (0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e23.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25 (0.20\u0026ndash;0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.68 (0.59\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eModel fit: χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eWLSMV\u003c/sub\u003e(5)\u0026thinsp;=\u0026thinsp;10.137, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.071, CFI\u0026thinsp;=\u0026thinsp;0.990, TLI\u0026thinsp;=\u0026thinsp;0.980, RMSEA\u0026thinsp;=\u0026thinsp;0.063 (90% CI: 0.000-0.119), SRMR\u0026thinsp;=\u0026thinsp;0.023\u003c/p\u003e \u003cp\u003eRaykov\u0026rsquo;s composite reliability: 0.75 (0.70\u0026ndash;0.79), Cronbach\u0026rsquo;s alpha: 0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5. Appraisal of health information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI compare health information from different\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.58 (0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e16.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47 (0.40\u0026ndash;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70 (0.62\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhen I see new information about health, I\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.66 (0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39 (0.33\u0026ndash;0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65 (0.57\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI always compare health information from\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.57 (0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43 (0.37\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.71 (0.64\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI know how to find out if the health\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.54 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e15.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48 (0.42\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72 (0.65\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI ask healthcare providers about the quality\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.70 (0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e15.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37 (0.31\u0026ndash;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.73 (0.66\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eModel fit: χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eWLSMV\u003c/sub\u003e(5)\u0026thinsp;=\u0026thinsp;12.108, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033, CFI\u0026thinsp;=\u0026thinsp;0.991, TLI\u0026thinsp;=\u0026thinsp;0.981, RMSEA\u0026thinsp;=\u0026thinsp;0.074 (90% CI: 0.019\u0026ndash;0.128), SRMR\u0026thinsp;=\u0026thinsp;0.023\u003c/p\u003e \u003cp\u003eRaykov\u0026rsquo;s composite reliability: 0.79 (0.75\u0026ndash;0.83), Cronbach\u0026rsquo;s alpha: 0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePart 2 Scales (Score range: 1 to 5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6. Ability to actively engage with healthcare providers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMake sure that healthcare providers understand\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.89 (1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70 (0.65\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78 (0.72\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeel able to discuss your health concerns with a\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.29 (1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54 (0.48\u0026ndash;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81 (0.76\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHave good discussions about your health\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.22 (1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60 (0.54\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81 (0.76\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiscuss things with healthcare providers\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.91 (1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69 (0.63\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75 (0.69\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsk healthcare providers questions to get\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.14 (1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62 (0.56\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80 (0.75\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eModel fit: χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eWLSMV\u003c/sub\u003e(5)\u0026thinsp;=\u0026thinsp;20.175, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, CFI\u0026thinsp;=\u0026thinsp;0.992, TLI\u0026thinsp;=\u0026thinsp;0.984, RMSEA\u0026thinsp;=\u0026thinsp;0.108 (90% CI: 0.062\u0026ndash;0.159), SRMR\u0026thinsp;=\u0026thinsp;0.022\u003c/p\u003e \u003cp\u003eRaykov\u0026rsquo;s composite reliability: 0.87 (0.84\u0026ndash;0.90), Cronbach\u0026rsquo;s alpha: 0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7. Navigating the healthcare system\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFind the right healthcare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.72 (1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e21.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67 (0.62\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.79 (0.74\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGet to see the healthcare providers I need to\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.03 (1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67 (0.61\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76 (0.70\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecide which healthcare provider you need\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.06 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65 (0.59\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85 (0.81\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMake sure you find the right place to get\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.22 (1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e16.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56 (0.50\u0026ndash;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85 (0.81\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFind out what healthcare services you are\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.80 (1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74 (0.69\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75 (0.69\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWork out what is the best care for you\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.97 (1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68 (0.62\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77 (0.71\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eModel fit: χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eWLSMV\u003c/sub\u003e(9)\u0026thinsp;=\u0026thinsp;13.015, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.162, CFI\u0026thinsp;=\u0026thinsp;0.999, TLI\u0026thinsp;=\u0026thinsp;0.998, RMSEA\u0026thinsp;=\u0026thinsp;0.041 (90% CI: 0.000-0.087), SRMR\u0026thinsp;=\u0026thinsp;0.014\u003c/p\u003e \u003cp\u003eRaykov\u0026rsquo;s composite reliability: 0.90 (0.88\u0026ndash;0.92), Cronbach\u0026rsquo;s alpha: 0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8. Ability to find good health information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFind information about health problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.92 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64 (0.58\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77 (0.71\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFind health information from several\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.97 (1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67 (0.62\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.67 (0.60\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGet information about health so you are\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.10 (1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60 (0.54\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.79 (0.73\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGet health information in words you\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.06 (1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62 (0.56\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80 (0.74\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGet health information by yourself\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.90 (1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70 (0.65\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77 (0.71\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eModel fit: χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eWLSMV\u003c/sub\u003e(5)\u0026thinsp;=\u0026thinsp;6.881, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.230, CFI\u0026thinsp;=\u0026thinsp;0.999, TLI\u0026thinsp;=\u0026thinsp;0.997, RMSEA\u0026thinsp;=\u0026thinsp;0.038 (90% CI: 0.000-0.100), SRMR\u0026thinsp;=\u0026thinsp;0.011\u003c/p\u003e \u003cp\u003eRaykov\u0026rsquo;s composite reliability: 0.85 (0.82\u0026ndash;0.88), Cronbach\u0026rsquo;s alpha: 0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9. Understanding health information well enough to know what to do\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConfidently fill medical forms in the correct\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.11 (1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63 (0.57\u0026ndash;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74 (0.67\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccurately follow the instructions from\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.14 (1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65 (0.59\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.71 (0.64\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRead and understand written health\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.23 (1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e17.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56 (0.50\u0026ndash;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85 (0.81\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRead and understand all the information on\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.66 (1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e30.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67 (0.61\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.86 (0.81\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderstand what healthcare providers are\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.16 (1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60 (0.54\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76 (0.70\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eModel fit: χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eWLSMV\u003c/sub\u003e(5)\u0026thinsp;=\u0026thinsp;11.429, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044, CFI\u0026thinsp;=\u0026thinsp;0.996, TLI\u0026thinsp;=\u0026thinsp;0.993, RMSEA\u0026thinsp;=\u0026thinsp;0.070 (90% CI: 0.011\u0026ndash;0.125), SRMR\u0026thinsp;=\u0026thinsp;0.016\u003c/p\u003e \u003cp\u003eRaykov\u0026rsquo;s composite reliability: 0.87 (0.85\u0026ndash;0.90), Cronbach\u0026rsquo;s alpha: 0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Floor or ceiling effect: \u0026ge; 15.0% of participants endorsed the highest response option (Strongly agree/Always easy) or lowest response option (Strongly disagree/Cannot do or always difficult) respectively. Items with floor or ceiling effects are in bold.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e^Difficulty is defined as the proportion of participants responding to Strongly disagree and Disagree for Scales 1 to 5. For Scales 6 to 9, it was calculated by the proportion of participants answering Cannot do or always difficult and Usually difficulty and Sometimes difficult.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNotes:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eCI: Confidence interval; χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eWLSMV\u003c/sub\u003e: Chi-square test using weighted least squares mean and variance adjusted estimator; CFI: Comparative Fit Index; TLI: Tucker\u0026ndash;Lewis index; RMSEA: root mean square error of approximation; SRMR: standardized root mean square residual.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eItems are truncated. Contact the HLQ developers for the full items.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[insert Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eItem difficulty\u003c/p\u003e \u003cp\u003eFor Scales 1 to 5, Scale 3. \u0026lsquo;Actively managing my health\u0026rsquo; had the smallest range of difficulty (37% to 45%), with item 3.4 \u0026lsquo;I set my own goals about health and fitness\u0026rsquo; being the most difficult (45% answered Strongly disagree or Disagree). Scale 4. \u0026lsquo;Social support for health\u0026rsquo; had the widest range of difficulty (25% to 42%) while this scale could also be considered the \u0026lsquo;easiest scale\u0026rsquo; as it was the only scale with two items that had difficulty levels below 30%.\u003c/p\u003e \u003cp\u003eWith Part 2 scales, Scale 8 \u0026lsquo;Ability to find good health information\u0026rsquo; had items with the smallest difficulty range (60% to 70%), while the widest difficulty range was observed for Scale 7 \u0026lsquo;Navigating the healthcare system\u0026rsquo; (56% to 74%). The difficulty levels for items in Part 2 were all above 50% (i.e., more than 50% answered Cannot do or always difficult or Usually difficult or Sometimes difficult). Scale 7 item 7.5 \u0026lsquo;Find out what healthcare services you are entitled to\u0026rsquo; had the highest difficulty level of 74% among all Part 2 items (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConfirmatory factor analysis\u003c/p\u003e \u003cp\u003eFor the one-factor models, model fit was satisfactory to reasonable with no large MIs with SEPC\u0026thinsp;\u0026gt;\u0026thinsp;0.20 except for Scale 3 \u0026lsquo;Actively managing my health\u0026rsquo;. For the initial fit, the chi-square test results indicated satisfactory fit (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) for five scales (Scales 1, 2, 4, 7 and 8) while CFI and TLI suggested satisfactory fit (\u0026gt;\u0026thinsp;0.95) for all scales except Scale 3. The RMSEA suggested satisfactory fit (\u0026lt;\u0026thinsp;0.06) for four scales (Scales 1, 2, 7 and 8) and \u0026lsquo;reasonable\u0026rsquo; fit for another three scales (Scales 4, 5 and 7). Initial model fit for Scale 3 was poor with two large MIs with SEPC\u0026thinsp;\u0026gt;\u0026thinsp;0.20 identified. A modified model including two correlated residuals (item 3.3 \u0026lsquo;Despite other things in my life, I make time to be healthy\u0026rsquo; with 3.1 \u0026lsquo;I spend quite a lot of time actively managing my health\u0026rsquo; with correlated residual\u0026thinsp;=\u0026thinsp;0.34; and item 3.3 \u0026lsquo;Despite other things in my life, I make time to be healthy\u0026rsquo; with 3.2 \u0026lsquo;I make plans for what I need to do to be healthy\u0026rsquo; with correlated residual\u0026thinsp;=\u0026thinsp;0.23) resulted in excellent fit (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFactor loadings for the one-factor models were above 0.60 except for two items of Scale 3 and one item for Scale 4. Item 3.3 \u0026lsquo;Despite other things in my life, I make time to be healthy\u0026rsquo; had a loading of 0.42 after model adjustment. The other two items, 3.1 \u0026lsquo;I spend quite a lot of time actively managing my health from Scale 3 (factor loading\u0026thinsp;=\u0026thinsp;0.56) and 4.4 \u0026lsquo;I have at least one person who can come to medical appointments with me\u0026rsquo; from Scale 4 (factor loading\u0026thinsp;=\u0026thinsp;0.58) were both above 0.50 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe nine-factor restrictive model fitted the data well: χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eWLSMV\u003c/sub\u003e(866)\u0026thinsp;=\u0026thinsp;1371.788, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000, CFI\u0026thinsp;=\u0026thinsp;0.970, TLI\u0026thinsp;=\u0026thinsp;0.967, RMSEA\u0026thinsp;=\u0026thinsp;0.047 (90% CI: 0.043\u0026ndash;0.052), and SRMR\u0026thinsp;=\u0026thinsp;0.053. Yet, some moderately large MIs with SEPC\u0026thinsp;\u0026gt;\u0026thinsp;0.20 were observed, indicating potential cross-loadings, including item 7.1 \u0026lsquo;Find the right healthcare\u0026rsquo; from scale 7 with Scale 1 \u0026lsquo;Feeling understood and supported by healthcare providers\u0026rsquo; (MI\u0026thinsp;=\u0026thinsp;25.62, SEPC\u0026thinsp;=\u0026thinsp;0.37). An adjusted model was fitted to the data, but no model improvement was observed. While this adjusted model resulted in less MIs with SEPC\u0026thinsp;\u0026gt;\u0026thinsp;0.20, there was still one large MI with SEPC\u0026thinsp;\u0026gt;\u0026thinsp;0.20. A second model was tested by adding a cross-loading of item 5.5 \u0026lsquo;I ask healthcare providers about the quality of health information I find\u0026rsquo; of Scale 5 with Scale 6 \u0026lsquo;Ability to actively engage with healthcare providers\u0026rsquo; (MI\u0026thinsp;=\u0026thinsp;15.53, SEPC\u0026thinsp;=\u0026thinsp;0.34). Again, the model could not be improved. Therefore, it was decided that the original model was adequate. Factor loadings of all items in the nine-factor model were above 0.60 except for three items (two items from Scale 4 and one item from Scale 5) but were all above 0.50. See Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eFactor loadings of the nine-factor model of the Health Literacy Questionnaire (HLQ) in Hindi\u003c/b\u003e Model fit: χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eWLSMV\u003c/sub\u003e (866)\u0026thinsp;=\u0026thinsp;1371.788, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000, CFI\u0026thinsp;=\u0026thinsp;0.970, TLI\u0026thinsp;=\u0026thinsp;0.967, RMSEA\u0026thinsp;=\u0026thinsp;0.047 (90% CI: 0.043\u0026ndash;0.052), SRMR\u0026thinsp;=\u0026thinsp;0.053\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Feeling understood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2. Sufficient information\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3. Actively managing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4. Social support\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5. Appraisal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6. Actively engage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7. Navigate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8. Find good information\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9. Understand information\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e 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colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e 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align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e 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align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[insert Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eDiscriminant validity\u003c/p\u003e \u003cp\u003eBased on the nine-factor model, inter-factor correlations ranged from 0.70 (Scales 1 and 9) to 1.04 (Scales 7 and 8). Out of the 36 pairs of correlations, eight pairs (Scales 2 and 3, 2 and 5, 6 and 7, 6 and 8, 6 and 9, 7 and 8, 7 and 9, and 8 and 9) were \u0026gt;\u0026thinsp;0.95 while 15 pairs from both Part 1 and Part 2 scales were \u0026gt;\u0026thinsp;0.80, indicating insufficient discriminant validity and possible presence of higher-order factor(s) among some of the scales. See Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInter-factor correlations of the Health Literacy Questionnaire (HLQ) scales based on nine-factor confirmatory factor analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Feeling understood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2. Sufficient information\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3. Actively managing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4. Social support\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5. Appraisal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6. Actively engage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7. Navigate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8. Find good information\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2. Sufficient information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.95\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3. Actively managing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.82\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4. Social support\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.88\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.82\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.89\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5. Appraisal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.90\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.93\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.81\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6. Actively engage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.82\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.82\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7. Navigate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.85\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.83\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8. Find good information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.85\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.83\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.82\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9. Understand information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: Inter-factor correlations\u0026thinsp;\u0026gt;\u0026thinsp;0.80 are in italics and underlined, while\u0026thinsp;\u0026gt;\u0026thinsp;0.95 are in bold.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eReliability\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Raykov\u0026rsquo;s composite reliability of Scales 1 to 5 ranged from 0.74 (Scales 1 and 3) to 0.79 (Scale 5), demonstrating acceptable reliability. For Scales 6 to 9, the results ranged from 0.85 (Scale 8) to 0.90 (Scale 7), indicating good reliability. Results of Cronbach\u0026rsquo;s alpha were similar to that of Raykov\u0026rsquo;s composite reliability.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study applied a rigorous process to translate and culturally adapt the HLQ into Hindi for application in rural India. The results show that the Hindi version of the HLQ demonstrates strong psychometric properties in a setting that is vastly different from the Australian setting where the tool was originally developed. The solid validity evidence implicates that the HLQ Hindi version will be a valuable tool to gain insights into the health literacy strengths and challenges of people living in the resource-poor rural India setting with diverse demographics. It can be used to identify interventions to support their health literacy development.\u003c/p\u003e \u003cp\u003eThe cognitive interviews found that the translated version was generally well-understood. Only one translated term, \u0026lsquo;healthcare providers\u0026rsquo;, caused some confusion and was revised to a general Hindi term for \u0026lsquo;doctors\u0026rsquo;. People in the rural and peri-urban areas in India commonly refer to anyone who gives them medicines as \u0026lsquo;doctors\u0026rsquo; \u0026ndash; this includes qualified medical officers, chemists, paramedical workers and even informal providers. Primary Health Centres, which are the main and often nearest source of care, usually have only one or two medical officers, while services like physiotherapy or dietetics are rarely available in government services. As a result, the term \u0026lsquo;healthcare providers\u0026rsquo; was unfamiliar, and participants related more easily to the word \u0026lsquo;doctors\u0026rsquo;. As such, the selected Hindi term of \u0026lsquo;doctors\u0026rsquo; was regarded as the most appropriate equivalent to the English. It is worth noting that such a substitution may not be necessary when applying the HLQ in urban areas, where the community\u0026rsquo;s knowledge of healthcare roles is likely to be more distinct.\u003c/p\u003e \u003cp\u003eAs expected in a marginalised rural community, the observed ceiling effects in the HLQ are small. Ceiling effects were identified in Scale 4 \u0026lsquo;Social support for health\u0026rsquo;, indicating that for many participants, getting social support is easy for participants to achieve. This ceiling effect likely reflects the communal and family-oriented culture in rural India, where individuals often live in joint families and maintain close ties with neighbours. This social structure ensures that people usually have someone to accompany them to medical appointments or help them when needed, aligning with the supportive behaviours measured in this HLQ scale. Similar findings were observed in our previous study in another village of Chandigarh [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], where participants also reported high levels of social support for health.\u003c/p\u003e \u003cp\u003eSurprisingly, some marginal ceiling effects were observed for Scale 5 \u0026lsquo;Appraisal of health information\u0026rsquo;. The sample did include people who were well educated, with 21.1% of participants had undergraduate or postgraduate degrees. For example, in the cognitive interview, a college graduate showed confidence in all Scale 5 items and mentioned using google to verify health information. Another plausible explanation could be that, in this rural context, people relied on trusted local sources (e.g., families, doctors or health workers) rather than independently appraising diverse or conflicting health information, thus perceiving themselves as competent without encountering much uncertainty. For example, participants of the cognitive interviews indicated that they would ask their families or doctors when responding to items in Scale 5. It is also possible that the issue of misinformation from the internet is less prominent in this resource-poor rural setting.\u003c/p\u003e \u003cp\u003eOn the other hand, no scales exhibited floor effects for Part 1 scales. For Part 2 scales, two items \u0026ndash; 7.1 \u0026lsquo;Find the right healthcare\u0026rsquo; of Scale 7 (21.5%) and 9.4 \u0026lsquo;Read and understand all the information on medication labels\u0026rsquo; of Scale 9 (30.4%) \u0026ndash; were highly endorsed at the lowest end (Cannot do or always difficult). The floor effect in both items may be attributed to low literacy as 31% of participants had below primary school education. Besides, there was also the high proportion of women (65%) in the survey sample. Women often face inequitable access to care and dependency on males to access healthcare services in India [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This was also reflected in the cognitive interviews when women tended to mention asking family or spouse in their responses.\u003c/p\u003e \u003cp\u003eWhile ceiling and floor effects were found for some items, the results of item difficulty indicated that each scale has items representing a range of difficulty, enabling participants to express their level of health literacy across all constructs. While Scale 5 \u0026lsquo;Appraisal of health information\u0026rsquo; of the Australian English HLQ had the most difficult items in Part 1 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], Scales 2, 3 and 5 of the Hindi version all had three items with item difficulty level above 40%. Besides, item difficulty levels of the Hindi version are all much higher than the Australian English version, especially for the items in Part 2, reflecting the limited resources in the rural setting and low education of most participants.\u003c/p\u003e \u003cp\u003eUsing a restrictive estimation approach, the nine one-factor models of the HLQ Hindi version demonstrate excellent to acceptable fit except for Scale 3 \u0026lsquo;Actively managing my health\u0026rsquo;. Based on the MIs, two correlated residuals were tested in a modified model. The pattern of a cluster of three items for Scale 3 suggest that there may be conceptual overlap among these items that is independent of the primary construct. Clearly, these items do capture distinct aspects about managing health: item 3.3 highlights goal setting as a motivator for self-care behaviours; item 3.1 reflects ongoing effort in managing health; and item 3.2 emphasizes structured planning for health management. The correlated residuals may be caused by some common conceptions related to the terms used for time and planning in Hindi language, such as \u0026lsquo;make time\u0026rsquo;, \u0026lsquo;spend quite a lot of time\u0026rsquo; or \u0026lsquo;make plans\u0026rsquo;. While the modifications do lead to an excellent fit, future qualitative and quantitative validity testing of the Hindi version of the HLQ may help identify possible wording improvement or if this conceptual overlap leads to construct imprecision.\u003c/p\u003e \u003cp\u003eOverall, factor loadings of the one-factor models are \u0026gt;\u0026thinsp;0.60 except for three items, of which two items were close to the threshold of 0.6, indicating strong association between the items and their relevant constructs. The only item with a low loading is item 3.3 (0.42), which is a result of model adjustment.\u003c/p\u003e \u003cp\u003eAn important finding was that the data fitted well to a highly restrictive nine-factor model. While we identified some large MIs which indicate the potential of cross-loadings in the model results, modifications did not lead to any model improvement or lowering of the inter-factor correlations. Future validity testing may consider using Bayesian Structural Equation Modeling to further explore potential cross loadings [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe high inter-factor correlations were as expected given the use of a highly restrictive nine-factor model. Most HLQ validity studies including the original Australian English version [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] have also found insufficient discriminant validity among the Part 2 scales. This led to the suggestion by Elsworth et al that there is possibly a higher-order factor of personal agency and efficacy, particularly among the items that constitute the Part 2 scales [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. However, for this study, higher inter-factor correlations were also observed between Part 1 scales. This suggests that there may be another higher-order factor present or a general factor running through all items in this particular setting. As indicated in the cognitive interview data, there is a strong reliance on the social and healthcare networks even when participants responded to items related to finding (Scale 8), understanding (Scale 9) and appraising (Scale 5) health information, navigating the healthcare system (Scale 7) or managing their health (Scale 3). The results illustrate how health is often seen as a collective effort. In rural areas, where resources may be limited and health literacy is variable, individuals may rely heavily on family or friends or healthcare providers for guidance, creating a sense of shared responsibility in managing health. This communal efficacy where individuals depend on others for health-related tasks, may lead to high correlation between the HLQ scales. Using a Bayesian approach for the multifactor CFA in future validity testing may provide more insights into the inter-factor correlations when the potential cross-loadings (and correlated residuals) are accounted for [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile high inter-factor correlations may indicate construct overlap in some context, another study using the HLQ in the same region found that the nine scales tend to be associated with different exogenous variables such as age, education, socio-economic class or having chronic illness. Further exploration of the data using cluster analysis identified eight clusters of people with varying higher or lower scores across the nine scales [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These results provide substantial support for the discriminant validity of HLQ scales. Further studies to explore the presence of high inter-factor correlations, using other discriminant validity testing methods, such as the Fornell and Larcker method [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] or the more recent Ronkko and Cho method [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], are recommended to gain deeper insights into this unique setting.\u003c/p\u003e \u003cp\u003eStrengths and limitations\u003c/p\u003e \u003cp\u003eThis study followed a rigorous procedure to evaluate the psychometric properties of the HLQ in Hindi. By integrating qualitative and quantitative data, we also demonstrated how validity evidence can be systematically developed for a health measure. For data collection, we incorporated an interview-based method for participants who were unable to complete the questionnaire. As such, people with limited reading and writing skills, which amounted to close to half of the survey participants, were included in the survey. However, the study also had some limitations. While Hindi is spoken and understood in the northern regions of India, the Hindi version of the HLQ may not be as applicable in other regions. The selection of the Chandigarh Union Territory of India was based on the fact that there was already a good rapport between the community and the researchers. While the goodwill could support data collection, it may also lead to social desirability bias. However, given the mean scale scores are generally lower (below 3.00 indicating \u0026lsquo;Agree\u0026rsquo; for items of Part 1 scales and below 4.00 indicating \u0026lsquo;Usually easy\u0026rsquo; for items of Part 2 scales, see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the effect of social desirability seemed to be limited. There is also the potential of clustering effect given our sampling method, despite efforts to ensure participants were interviewed separately. Besides, we conducted the study among the general population where participants were comparatively healthier than people recruited from a healthcare facility. Also, the sample was younger (61.5% below the age of 40 years) than the general population. Therefore, the HLQ data should be interpreted with caution when it is used to assess the health literacy needs of populations with other socio-demographic and health profiles.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe HLQ was found to have strong psychometric properties when it was first developed and in versions later adapted and translated for different settings. However, to use it in the Indian rural setting with confidence, validity testing is required to ensure the data collected can be interpreted and used to make meaningful and equitable public health decisions in the Indian rural context. This study showed that the Hindi HLQ possesses the same strong internal structure and reliability as the Australian English HLQ. The findings of this study indicate that the Hindi HLQ can be used in a Hindi speaking community in northern India to assess health literacy strengths and challenges. It will help inform the development of public health interventions to improve health and equity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHLQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Literacy Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfirmatory factor analysis (CFA)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePGIMER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePost-Graduate Institute of Medical Education and Research\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWLSMV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeighted least squares mean and variance adjusted\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMPHW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedical Officer and Multi-Purpose Health Worker\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTranslation Integrity Protocol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComparative Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTLI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTucker-Lewis Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoot Mean-Square Error of Approximation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSRMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandardised Root Mean Square Residual\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emodification indices\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandardised expected parameter change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe study was approved by the Institute Ethics Committee, Post-Graduate Institute of Medical Education and Research Chandigarh (INT/IEC/2019/000414). It was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments. Before participation in the cognitive interview or the survey, informed consent was obtained from all participants after briefing them about the study purpose and that participation was voluntary.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe dataset generated and analysed are not publicly available due to the privacy of survey participants but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Indian Council of Medical Research Fellowship to RP; and a\u0026nbsp;National Health and Medical Research Council Investigator Grant [2025522 to CC, MH and RHO].\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eFunding acquisition: RP, RHO; study conceptualisation: MK, RP, RHO; data curation: RP; Methodology: MK, MG, SK, RK, RP, CC, GE, RHO; data analysis and interpretation of results: CC, GE, MK, RK, RP, MH, RHO; manuscript draft and preparation: RP, CC, MK, RK All authors reviewed and approved the final version of this manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe would like to thank the Medical Officer and Multi-Purpose Health Workers of the Civil Dispensary in the selected village for their support to access the households. We are grateful to the nursing students who helped us in data collection. Most of all, we would like to thank the respondents who participated in the cognitive interviews and cross-sectional survey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eParker RM, Baker DW, Williams MV, Nurss JR. The test of functional health literacy in adults: a new instrument for measuring patients' literacy skills. J Gen Intern Med. 1995;10:537\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF02640361\u003c/span\u003e\u003cspan address=\"10.1007/BF02640361\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRowlands G, Khazaezadeh N, Oteng-Ntim E, Seed P, Barr S, Weiss BD. Development and validation of a measure of health literacy in the UK: the newest vital sign. BMC Public Health. 2013;13:1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2458-13-116\u003c/span\u003e\u003cspan address=\"10.1186/1471-2458-13-116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Health literacy development for the prevention and control of noncommunicable diseases:Volume 2. A globally relevant perspective. Licence: CC BY- NC- SA 3.0 IGO. Geneva: World Health Organization.; 2022 [cited 2025 June 17]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/9789240055339\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/9789240055339\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Health literacy Geneva: World Health Organization; 2022 [cited 2025 April 15]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/health-literacy\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/health-literacy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborne RH, Batterham RW, Elsworth GR, Hawkins M, Buchbinder R. The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ). BMC Public Health. 2013;13:1\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2458-13-658\u003c/span\u003e\u003cspan address=\"10.1186/1471-2458-13-658\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWahl A, Hermansen \u0026Aring;, Osborne RH, Larsen MH. A validation study of the Norwegian version of the Health Literacy Questionnaire: a robust nine-dimension factor model. journalssagepubcom. 2020;49:471\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1403494820926428\u003c/span\u003e\u003cspan address=\"10.1177/1403494820926428\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDebussche X, Lenclume V, Balcou-Debussche M, Alakian D, Sokolowsky C, Ballet D, et al. Characterisation of health literacy strengths and weaknesses among people at metabolic and cardiovascular risk: Validity testing of the Health Literacy Questionnaire. SAGE open Med. 2018;6:2050312118801250. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2050312118801250\u003c/span\u003e\u003cspan address=\"10.1177/2050312118801250\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNolte S, Osborne RH, Dwinger S, Elsworth GR, Conrad ML, Rose M, et al. German translation, cultural adaptation, and validation of the Health Literacy Questionnaire (HLQ). PLoS ONE. 2017;12:1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0172340\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0172340\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaindal HT, Kayser L, Norgaard O, Bo A, Elsworth GR, Osborne RH. Cultural adaptation and validation of the Health Literacy Questionnaire (HLQ): robust nine-dimension Danish language confirmatory factor model. Springerplus. 2016;5:1232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40064-016-2887-9\u003c/span\u003e\u003cspan address=\"10.1186/s40064-016-2887-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolarcik P, Cepova E, Madarasova Geckova A, Elsworth GR, Batterham RW, Osborne RH. Structural properties and psychometric improvements of the Health Literacy Questionnaire in a Slovak population. Int J Public Health 2017. 2017;62:5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/S00038-017-0945-X\u003c/span\u003e\u003cspan address=\"10.1007/S00038-017-0945-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark JH, Osborne RH, Kim HJ, Bae SH. Cultural and linguistic adaption and testing of the Health Literacy Questionnaire (HLQ) among healthy people in Korea. PLoS ONE. 2022;17:e0271549. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0271549\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0271549\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHa Dinh TT, Bonner A. Psychometric properties of the health literacy questionnaire tested in Vietnamese adults with chronic diseases. BMC Public Health. 2025;25:44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-024-21156-7\u003c/span\u003e\u003cspan address=\"10.1186/s12889-024-21156-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoraes KL, Brasil VV, Mialhe FL, De Carvalho Sampaio HA, Sousa ALL, Canhestro MR et al. Validation of the Health Literacy Questionnaire (HLQ) to brazilian portuguese. Acta Paulista de Enfermagem: Escola Paulista de Enfermagem, Universidade Federal de S\u0026atilde;o Paulo; 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.37689/ACTA-APE/2021AO02171\u003c/span\u003e\u003cspan address=\"https://doi:10.37689/ACTA-APE/2021AO02171\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoateng MA, Agyei-Baffour P, Angel S, Enemark U. Translation, cultural adaptation and psychometric properties of the Ghanaian language (Akan; Asante Twi) version of the Health Literacy Questionnaire. BMC Health Serv Res: BioMed Central Ltd; 2020. pp. 1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1186/S12913-020-05932-W/TABLES/4\u003c/span\u003e\u003cspan address=\"https://doi:10.1186/S12913-020-05932-W/TABLES/4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborne RH, Cheng CC, Nolte S, Elmer S, Besancon S, Budhathoki SS, et al. Health literacy measurement: embracing diversity in a strengths-based approach to promote health and equity, and avoid epistemic injustice. BMJ Global Health. 2022;7:e009623. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjgh-2022-009623\u003c/span\u003e\u003cspan address=\"10.1136/bmjgh-2022-009623\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Educational Research Association, American Psychological Association. National Council on Measurement in Education. Standards for educational and psychological testing. Washington (DC): American Educational Research Association; 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeWalt DA, Rothrock N, Yount S, Stone AA. Evaluation of item candidates: the PROMIS qualitative item review. Med Care. 2007;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/01.mlr.0000254567.79743.e2\u003c/span\u003e\u003cspan address=\"10.1097/01.mlr.0000254567.79743.e2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. :S12-21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeatty PC, Willis GB. Research synthesis: The practice of cognitive interviewing. Pub Opin Q. 2007;71:287\u0026ndash;311. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/poq/nfm006\u003c/span\u003e\u003cspan address=\"10.1093/poq/nfm006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeslie CJ, Hawkins M, Smith DL. Using the health literacy questionnaire (HLQ) with providers in the early intervention setting: a qualitative validity testing study. Int J Environ Res Public Health. 2020;17:2603. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph17072603\u003c/span\u003e\u003cspan address=\"10.3390/ijerph17072603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang X, Yang Y. An evaluation of WLSMV and Bayesian methods for confirmatory factor analysis with categorical indicators. Int J Quant Res Educ. 2014;2:17\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1504/IJQRE.2014.060972\u003c/span\u003e\u003cspan address=\"10.1504/IJQRE.2014.060972\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoshagen M, Musch J. Sample size requirements of the robust weighted least squares estimator. Methodology. 2014. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1027/1614-2241/a000068\u003c/span\u003e\u003cspan address=\"10.1027/1614-2241/a000068\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang T. Relative Performance of MLR, WLSMV, and Bayes Estimators: An Investigation Using Confirmatory Factor Analysis with Ordinal Data. University of South Carolina; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborne RH, Elsworth ES, Hawkins M, Cheng C. The Ophelia Manual: The Optimising Health Literacy and Access (Ophelia) process to plan and implement National Health Literacy Demonstration Projects. Melbourne, Australia: Centre for Global Health and Equity, School of Health Sciences, Swinburne University of Technology; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHawkins M, Cheng C, Elsworth GR, Osborne RH. Translation method is validity evidence for construct equivalence: analysis of secondary data routinely collected during translations of the Health Literacy Questionnaire (HLQ). 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12874-020-00962-8\u003c/span\u003e\u003cspan address=\"10.1186/s12874-020-00962-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrisko JW, Maschi T. Content analysis: Oxford University Press; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIBM Corp. IBM SPSS Statistics for Windows, Version 29.0.2.0. Armonk. NY: IBM Corp.; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuth\u0026eacute;n L, Muth\u0026eacute;n B. \u0026amp;. Mplus User\u0026rsquo;s Guide, Eighth Edi. ed. Muth\u0026eacute;n 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeauchamp A, Buchbinder R, Dodson S, Batterham RW, Elsworth GR, McPhee C, et al. Distribution of health literacy strengths and weaknesses across socio-demographic groups: a cross-sectional survey using the Health Literacy Questionnaire (HLQ). BMC Public Health. 2015;15:678. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-015-2056-z\u003c/span\u003e\u003cspan address=\"10.1186/s12889-015-2056-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDo \u0026Oacute;DN, Goes AR, Elsworth G, Raposo JF, Loureiro I, Osborne RH. Cultural Adaptation and Validity Testing of the Portuguese Version of the Health Literacy Questionnaire (HLQ). Int J Environ Res Public Health. 2022;19:6465. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph19116465\u003c/span\u003e\u003cspan address=\"10.3390/ijerph19116465\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNolte S, Osborne RH, Dwinger S, Elsworth GR, Conrad ML, Rose M, et al. German translation, cultural adaptation, and validation of the Health Literacy Questionnaire (HLQ). PLoS ONE. 2017;12:e0172340. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0172340\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0172340\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFayers PM, Machin D. Quality of Life: The Assessment, Analysis and Interpretation of Patient-reported Outcomes. Wiley; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDriban JB, Morgan N, Price LL, Cook KF, Wang C. Patient-Reported Outcomes Measurement Information System (PROMIS) instruments among individuals with symptomatic knee osteoarthritis: a cross-sectional study of floor/ceiling effects and construct validity. BMC Musculoskelet Disord. 2015;16:253. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12891-015-0715-y\u003c/span\u003e\u003cspan address=\"10.1186/s12891-015-0715-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarin O. Ceiling Effect. Michalos AC. editor. Dordrecht: Springer; 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchreiber JB, Nora A, Stage FK, Barlow EA, King J. Reporting structural equation modeling and confirmatory factor analysis results: A review. J educational Res. 2006;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3200/JOER.99.6.323-338\u003c/span\u003e\u003cspan address=\"10.3200/JOER.99.6.323-338\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. :323\u0026thinsp;\u0026ndash;\u0026thinsp;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown TA. Confirmatory factor analysis for applied research. Guilford; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinney SJ, DiStefano C. Non-normal and categorical data in structural equation modeling. Struct equation modeling: second course. 2006;10:269\u0026ndash;314.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabachnick BG, Fidell LS, Ullman JB. Using multivariate statistics: pearson Boston, MA; 2007.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHair JF Jr, Anderson RE, Tatham RL, Black WC. Multivariate data analysis with readings. Prentice-Hall, Inc.; 1995.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDominguez-Lara S. Proposal for cut-offs for factor loadings: A construct reliability perspective. Enferm Clin (Engl Ed). 2018;28:401\u0026ndash;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enfcli.2018.06.002\u003c/span\u003e\u003cspan address=\"10.1016/j.enfcli.2018.06.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlavi M, Visentin DC, Thapa DK, Hunt GE, Watson R, Cleary M. Chi-square for model fit in confirmatory factor analysis. J Adv Nurs. 2020;76:2209\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jan.14399\u003c/span\u003e\u003cspan address=\"10.1111/jan.14399\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrowne MW, Cudeck R. Alternative ways of assessing model fit. Sociol methods Res. 1992;21:230\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0049124192021002005\u003c/span\u003e\u003cspan address=\"10.1177/0049124192021002005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu CY. Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes. University of California, Los Angeles; 2002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWest SG, Taylor AB, Wu W. Model fit and model selection in structural equation modeling. Handb Struct equation Model. 2012;1:209\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhittaker TA. Using the modification index and standardized expected parameter change for model modification. J Experimental Educ. 2012;80:26\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00220973.2010.531299\u003c/span\u003e\u003cspan address=\"10.1080/00220973.2010.531299\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaris WE, Satorra A, Van der Veld WM. Testing structural equation models or detection of misspecifications? Structural equation modeling 2009;16:561\u0026thinsp;\u0026ndash;\u0026thinsp;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10705510903203433\u003c/span\u003e\u003cspan address=\"10.1080/10705510903203433\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026ouml;nkk\u0026ouml; M, Cho E. An updated guideline for assessing discriminant validity. Organizational Res methods. 2022;25:6\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1094428120968614\u003c/span\u003e\u003cspan address=\"10.1177/1094428120968614\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarsh HW, Morin AJ, Parker PD, Kaur G. Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis. Ann Rev Clin Psychol. 2014;10:85\u0026ndash;110. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-clinpsy-032813-153700\u003c/span\u003e\u003cspan address=\"10.1146/annurev-clinpsy-032813-153700\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElsworth GR, Nolte S, Cheng C, Hawkins M, Osborne RH. Modelling variance in the multidimensional Health Literacy Questionnaire: Does a General Health Literacy factor account for observed interscale correlations? SAGE Open Med. 2022;10:20503121221124771. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/20503121221124771\u003c/span\u003e\u003cspan address=\"10.1177/20503121221124771\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaykov T. Scale construction and development using structural equation modeling. 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBell SM, Chalmers RP, Flora DB. The impact of measurement model misspecification on coefficient omega estimates of composite reliability. Educ Psychol Meas. 2024;84:5\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/00131644231155804\u003c/span\u003e\u003cspan address=\"10.1177/00131644231155804\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarstedt M, Ringle CM, Hair JF. Partial least squares structural equation modeling. Handbook of market research. Springer; 2021. pp. 587\u0026ndash;632.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHair JF Jr, Hult GTM, Ringle CM, Sarstedt M, Danks NP, Ray S et al. Evaluation of reflective measurement models. Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. 2021:75\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoyle GJ. Does item homogeneity indicate internal consistency or item redundancy in psychometric scales? Personality and in dividual differences 1991;12:291\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0191-8869(91)90115-R\u003c/span\u003e\u003cspan address=\"10.1016/0191-8869(91)90115-R\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePassi R, Kaur M, Lakshmi P, Cheng C, Hawkins M, Osborne RH. Health literacy strengths and challenges among residents of a resource-poor village in rural India: Epidemiological and cluster analyses. PLOS Global Public Health. 2023;3:e0001595. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pgph.0001595\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgph.0001595\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh S, Rajak B, Dehury RK, Mathur S, Samal A. Differential access of healthcare services and its impact on women in India: A systematic literature review. SN Social Sci. 2023;3:16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s43545-023-00607-9\u003c/span\u003e\u003cspan address=\"10.1007/s43545-023-00607-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElsworth GR, Beauchamp A, Osborne RH. Measuring health literacy in community agencies: a Bayesian study of the factor structure and measurement invariance of the health literacy questionnaire (HLQ). BMC Health Serv Res. 2016;16:508. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12913-016-1754-2\u003c/span\u003e\u003cspan address=\"10.1186/s12913-016-1754-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRademakers J, Waverijn G, Rijken M, Osborne R, Heijmans M. Towards a comprehensive, person-centred assessment of health literacy: translation, cultural adaptation and psychometric test of the Dutch Health Literacy Questionnaire. BMC Public Health. 2020;20:1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-020-09963-0\u003c/span\u003e\u003cspan address=\"10.1186/s12889-020-09963-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFornell C, Larcker DF. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J Mark Res. 1981;18:39\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/002224378101800104\u003c/span\u003e\u003cspan address=\"10.1177/002224378101800104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Health literacy, Health Literacy Questionnaire, HLQ, validity, rural setting, India, psychometric testing","lastPublishedDoi":"10.21203/rs.3.rs-8340440/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8340440/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHealth literacy is an important determinant of health. To assess health literacy, the Health Literacy Questionnaire (HLQ) was developed in 2013 in Australia. However, for the 9-scale HLQ to be used in a different socio-cultural environment, it is necessary to culturally adapt and test the questionnaire before implementation. This study aimed to adapt the HLQ to Hindi and evaluate its psychometric properties in an Indian rural, resource-poor setting, the Chandigarh Union Territory.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe translation was guided by the Translation Integrity Protocol, followed by a consensus meeting with the HLQ developer. Cognitive interviews were undertaken to collect validity evidence on response process. A cross-sectional survey was then conducted to evaluate the psychometric properties, including internal structure and reliability, of the HLQ Hindi version.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCognitive interviews (n\u0026thinsp;=\u0026thinsp;10) results indicated that revision to one term was required. A total of 260 adults participated in the survey (mean age: 36.9 years) with 61.5% being women. All one-factor confirmatory factor models demonstrated satisfactory to reasonable fit except Scale 3 \u0026lsquo;Actively managing my health\u0026rsquo; which achieved excellent fit following model adjustment. Factor loadings were all \u0026gt;\u0026thinsp;0.60 except for three items. The nine-factor model demonstrated close fit (χ\u0026sup2; WLSMV (866)\u0026thinsp;=\u0026thinsp;1371, p\u0026thinsp;=\u0026thinsp;0.000, CFI\u0026thinsp;=\u0026thinsp;0.970, TLI\u0026thinsp;=\u0026thinsp;0.967, RMSEA\u0026thinsp;=\u0026thinsp;0.047, SRMR\u0026thinsp;=\u0026thinsp;0.053). Insufficient discriminant validity was observed among most of the nine factors. Reliability was good, with Raykov\u0026rsquo;s composite reliability\u0026thinsp;\u0026gt;\u0026thinsp;0.70 for Scales 1 to 5 and \u0026gt;\u0026thinsp;0.80 for Scales 6 to 9.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe culturally adapted HLQ Hindi version was found to have strong psychometric properties in the Indian rural setting. It will be a valuable needs assessment tool to improve health equity and outcomes.\u003c/p\u003e","manuscriptTitle":"Cultural adaptation and validity testing of the Health Literacy Questionnaire (HLQ) in Hindi in northern India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 08:00:44","doi":"10.21203/rs.3.rs-8340440/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-17T09:57:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-17T03:46:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-17T03:45:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-12-11T23:52:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8fa7ac3-8eb3-48f0-86df-e940a737f661","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-13T14:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 08:00:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8340440","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8340440","identity":"rs-8340440","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}