Measuring well-being: Creating a taxonomy and item bank for positive mental health

Measuring well-being: Creating a taxonomy and item bank for positive mental health | 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 Article Measuring well-being: Creating a taxonomy and item bank for positive mental health Matthew Iasiello, Joep van Agteren, Kathina Ali, Elli Kolovos, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6862291/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The science of well-being has advanced rapidly, yet the field remains fragmented by inconsistent terminology and overlapping constructs; a challenge known as the “jingle-jangle” problem. To address this, we developed and calibrated item banks for 26 dimensions of positive mental health, aiming to provide a comprehensive and precise framework for assessment. Drawing from a pool of 3,555 items sources from existing scales identified in a prior review (Iasiello et al., 2024), we refined the selection to 224 items through rigorous content evaluation. These items were then calibrated using factor analysis, structural equation modeling, and item response theory in a diverse sample of American and Australian adults ( n =837). Findings supported the distinctiveness and separability of the 26 dimensions, enabling the creation of brief, precise measures. This multidimensional approach facilitates clearer communication between researchers, practitioners, and policymakers, enhancing the generalizability and replicability of well-being research and lays a foundation for a future taxonomy of positive mental health. Health sciences/Risk factors Biological sciences/Psychology positive mental health well-being mental well-being assessment quality of life resilience Figures Figure 1 Public Significance Statement Understanding and promoting positive mental health is crucial for enhancing individual and societal well-being. However, the field faces challenges due to inconsistent terminology and measurement. This study addresses these issues by creating a comprehensive set of item banks across 26 dimensions of positive mental health and tested them across American and Australian populations. This work aims to produce reliable tools for researchers, practitioners, and policymakers, thereby fostering better research, policy, and public understanding of positive mental health. Introduction Well-being science is a field aimed at understanding the full spectrum of human states and functioning from languishing to flourishing (Huppert, 2014; Keyes, 2002). For clarity, we refer to ‘positive states and functioning’ as positive mental health , a term that is often synonymous with mental well-being and more easily distinguished from mental illness. Possibly explained by the exponential growth in the field (Wang et al., 2023), confusion remains about how positive mental health should be defined and measured (Martela, 2024; VanderWeele et al., 2020). There is an ever-growing list of frameworks and measurement tools of positive mental health, and it is imperative that the field begins to distinguish between useful variety and unhelpful redundancy. The diversity in the field exists at four levels: (1) different theoretical views about what positive mental health is (Robbins & Friedman, 2018) , (2)the dimensions which constitute positive mental health (Martela & Sheldon, 2019; Novak et al., 2025), (3) inconsistent terminology to describe the dimensions (van Zyl & Rothmann, 2022), and (4) inconsistent measurement of the dimensions (Iasiello et al., 2024). Differences in theoretical views by which positive mental health can be considered are a natural and vital source of variety, as models have been developed within specific disciplines (i.e., economics vs psychology; Edmunds, 2010), with contrasting philosophical views (i.e., desire satisfaction vs mental state; Woodard, 2015). To date, it has been largely illuminating for the academic community to have research streams dedicated to each of these views, contributing different understandings of human nature (Allardt, 1976; Martela & Sheldon, 2019). However, confusion occurs when we merely label each of these as ‘well-being’ and do not differentiate between them. For example, the abstracts of two intervention studies might each report improvements in a sample’s positive mental health, only for reviewers to find that the improvements were in satisfaction with life in the first study, and agency and a sense of belonging in the second: blurring (in this case) the lines between hedonic and eudemonic well-being. The majority of models and frameworks of positive mental health are multi-dimensional, mental state models, which describe lists of dimensions that constitute positive mental health (Woodard, 2015). Again, there are appropriate reasons for diversity in these lists of dimensions, for example models developed for different cultural settings (Lomas, 2021) or for particular ages, demographics, or social groups (Alexandrova & Fabian, 2022). Unhelpful redundancy occurs when we maintain a range of partially-overlapping models of positive mental health, which are used interchangeably in practice. A review of measures of flourishing stated that “current dissensus [of dimensions] undermines the credibility of flourishing research and decreases the coherence and cumulation of the research domain” (Novak et al., 2025, p. 73). These often-arbitrary differences in combinations of dimensions are compounded by differences in terminology at the dimensional level, representing the third level of unhelpful redundancy (Fowers et al., 2024). Iasiello et al. (2024) reviewed measures of mental well-being, quality of life, and resilience/coping, finding 155 measures of positive mental health with more than 400 dimensions described by the measures. Despite differences in language and nomenclature, the list could be thematically synthesized into a coherent set of 21 dimensions. For example, some measures were said to measure Happiness as a dimension of positive mental health while others said to measure Positive Affect, while the items used to measure the dimensions were effectively identical. Finally, the way these dimensions are operationalized into measures can substantially differ. For example, Novak et al. (2025) compared the affective well-being items of flourishing measures, finding items such as “In general, how often do you feel positive?”, “‘I feel happy most of the time’”, and “In general, how happy or unhappy do you usually feel?”. Iasiello et al. (2024) found that the structure of well-being measures can differ considerably in Likert scale options, response formats and timeframe. The practical impact of these differences are poorly understood, and likely make comparisons between measures inappropriate (Hone et al., 2014). Confusion at these four levels exacerbates the “jingle-jangle” problem within well-being science (Disabato et al., 2025; Marsh et al., 2019), which has previously been attributed to vague connection between psychological theory and its operationalization in empirical studies (Hanfstingl et al., 2024). The jingle-jangle problem refers to a semantic issue where different terms are used to describe the same concept (jingle), or the same term is used for different concepts (jangle). For instance, “jingle” occurs when two researchers use different names for what is essentially the same psychological construct (e.g., curiosity, openness, and love for learning are treated as distinct in Peterson & Seligman, 2004). A “jangle” occurs when the same name is applied to difference concepts, such as positive affect being used indeterminately to refer to both ‘activated’ states such as joy and happiness or with ‘low-activation’ states such as calmness and serenity (LaRowe et al., 2024). Jingle-jangle fallacies have been discussed across well-being science, in joy (Schnitker et al., 2020), mindfulness (Altgassen et al., 2024), empathy (Ayache et al., 2024), and flourishing (van Zyl & Rothmann, 2022). The Current Study Clear terminology and precise, comparable, and comprehensive measurement tools are needed for the potential of well-being science to be realized by clinicians, policymakers, educators, and employers (Guðmundsdóttir, 2011; VanderWeele, 2017). This issue of conceptual ambiguity is neither new nor unique to the measurement of positive mental health (Pilkonis et al., 2011). Item banks have been used to solve similar terminology and measurement issues in areas across several health domains (Cella et al., 2019) and mental illness (Batterham et al., 2016). This study aims to develop and calibrate item banks using the measures and dimensions of positive mental health identified by Iasiello et al., (2024), resulting in brief measurement tools to enable consistency in assessment across multiple dimensions of positive mental health. Results Item Pool Construction The flow of items into pools, reallocation, winnowing, and selection is displayed in Table 1. A total of 3,555 items were extracted, following reallocation by three authors (interrater kappa ranging from .55 to .90 depending on the dimension), 2,518 items were available for winnowing and 515 non-discriminative and 522 unclear items were removed from the pools. Winnowing resulted in a total of 659 items selected for inclusion, with a range of 6-87 items per dimension. The variation in item count was due to the differing number of measures found per each dimension. After ranking items for face validity, the final item pool resulted in 228 items selected for testing, with a range of 4-24 items per dimension, which was dependent on the number of subdimensions identified within each of the dimensions, see Supplementary Materials (Table S1). Insert Table 1 here Validation of the Model The STROBE statement for the survey participants is depicted in Figure 1. Of the 2,192 participants who were assessed for eligibility, n = 837 were included in the study. Insert Figure 1 here The major reason for exclusion was failing the attention check (n = 513) and completing the survey too quickly (n = 406). Participant characteristics are detailed in Table 2. Insert Table 2 here Factor structure of the model: The overall CFA model demonstrated poor fit, 𝜒 2 (24205) = 57834.8, p <.001, RMSEA = .041 CI 90% [.040, .041], CFI = .825, TLI = .821, SRMR = .071. The ESEM model demonstrated acceptable fit, 𝜒 2 (19280) = 35905.0, p <.001, RMSEA = .032 CI 90% [.032, .033], CFI = .913, TLI = .889, SRMR = .014. Small-medium correlations were identified between the factors (as indicated in Supplementary Materials Table S2), with 6.7% (n = 22/325) of the factor pairs correlating > 0.5. Most of these medium sized factor pair correlations came from Self-acceptance (n = 9), Competence (n = 5), and Emotion-focused coping (n = 5). Within-Dimension Testing Indices of model fit for the initial models are provided in Table 3. Eight dimensions displayed a unidimensional structure with acceptable fit (i.e., Competence, Engagement, Life Satisfaction, Meaning and Purpose, Personal circumstances, Problem-focused coping, Self-congruence, and Vitality), while 10 were tested using exploratory analysis. There were three dimensions that showed evidence of over-fitting, based on RMSEA = 0 (i.e., Calmness, Development, and Happiness). Insert Table 3 here The items, their IRT parameters, including discrimination (slopes) and difficulty (thresholds), for the resulting item banks are provided in Supplementary Materials (Table S3). Fifty-two items were removed based on local dependence. Discussion The current study aimed to address the use of inconsistent terminology and overlapping constructs in measures of positive mental health. This is a barrier to academic communication and can derail progress in research and practice. In response, we developed calibrated item banks for 26 dimensions of positive mental health following established guidelines for item bank formation (Cella et al., 2010). This work offers researchers and clinicians brief, vetted tools that provide conceptual clarity and consistency in positive mental health assessment. The 26 item banks were developed from existing, validated measurement tools in the literature. Construction of the item pools revealed many shortfalls in measures, which, unfortunately permeate well-being science. For instance, 29% of items were removed from the item pools due to being either indiscriminate (meaning they could be misinterpreted as two different dimensions) or unclear. Similarly, the process revealed a large degree of semantically redundant items that exist among the many measures of positive mental health, indicating that there are few unique items being used despite the many available measures. To illustrate, semantically redundant life satisfaction items included: “I am satisfied with my current life”, “I am satisfied with my life”, “I felt satisfied with my life”, and “I am very satisfied with my life”. These issues are important for the future of well-being science, particularly in cross-cultural or heterogenous samples (Kiknadze & Fowers, 2023), as the interpretation, and therefore value, of these items may vary considerably between individuals, affecting the psychometric validity of the measure (i.e., measurement invariance, Putnick & Bornstein, 2016). As demonstrated by exploratory structural equation modelling (ESEM), there was acceptable model fit for the 26-factor model. This finding should not be interpreted as the authors establishing a ‘new’ 26-factor model of positive mental health, but instead to support the ‘separability’ of the 26-dimensions. The fact that such a multi-dimensional model showed acceptable fit with minimal correlation between factors is an endorsement of the rigorous item pool construction process. By carefully sorting items, the resultant banks appear to have avoided a well-established issue in well-being science, that latent factors are often highly correlated with each other, even when using a less stringent technique such as ESEM (Joshanloo & Jovanović, 2017). As discussed, measurement dissensus undermines the credibility and reliability of well-being science. The results of the current study lay the foundation for a formal taxonomy of positive mental health, mirroring successful developments in other health areas suffering inconsistent language use and overlapping models (i.e. behavior change; Michie et al., 2013). It is anticipated that the set of dimensions and calibrated item banks (see Supplementary Materials Table S3) can facilitate greater consistency in mental health research and intervention and enable innovation across a range of areas in the field. However, like other successful efforts to establish a taxonomy, we consider that the results of this study represent a starting point, and that multiple versions with transparent testing and consultation are required (for example, Atkins et al., 2017). In addition to clarifying the literature and enabling consistency in well-being science, the major applications of the item banks relate primarily to a shift from ‘overall well-being’ to a dimensional view (Joseph & Wood, 2010). For example, it has been demonstrated that positive mental health is a protective factor from future mental illness, and the absence of positive mental health exposes an individual to heightened risk (Burns et al., 2022; Keyes et al., 2010). However, it is not yet understood which dimensions (or combinations of dimensions) are most explanatory of this risk, or whether certain dimensions are more or less relevant for particular demographics. High quality assessment of the dimensions identified in the current study would enable greater precision could further assist in identifying people who are languishing but may not yet have symptoms of a mental illness (Rottenberg et al., 2018). Similarly, this separation of dimensions enables a greater understanding of intervention efficacy and mechanisms. While meta-analyses have demonstrated that overall well-being can be improved following interventions, much less is known about which interventions are effective at improving specific dimensions, and less again understood about the mechanisms by which effectiveness occurs (Schotanus-Dijkstra et al., 2019). This work need not be limited to psychological interventions, but also across all sociological levels, for example, where precise understanding of policies impacting specific dimensions of positive mental health would allow for more effective allocation of resources (Kinderman et al., 2015). These applications are also relevant for clinical research and practice (Goodman, 2025). Individuals rarely seek clinical treatment only to reduce mental health symptoms or distress; they also report a desire to improve aspects of positive mental health (Chevance et al., 2020; Rottenberg & Kashdan, 2022), and sometimes doing so is equally or more important to them than symptom reduction (Zimmerman et al., 2022). Therefore, to measure therapeutic success, interventionists need measurement tools to determine whether patients experienced reductions in illness symptoms and improvements in well-being, i.e., complete mental health (Keyes, 2003). Sound measures of positive mental health can also provide insight about how well a person responds to a particular treatment. For example, people with depression often experience deficits in positive affect, but traditional treatment focuses on reducing negative affect and distress (Devendorf et al., 2022). Newer treatments that incorporate interventions to increase positive emotions have shown promise in both reducing depression symptoms and improving well-being (Craske et al., 2019). Limitations and Future Directions The current study employed an empirically supported, data-driven approach to creating a taxonomy using systematic review principles based on published literature. There are several limitations related to the sources of items, the construction of the item pools and the calibration of the item banks, which have flow on effects to the resultant taxonomy. Firstly, the item pools were constructed from existing measures of positive mental health, which are predominantly Western-oriented, and the item pools were calibrated in a Western sample. There is an ongoing debate in the literature regarding the relative value of expert (i.e., top-down) and lay (bottom-up) perspectives and definitions of positive mental health (Willen et al., 2025). While many of the source measures of positive mental health have been validated in many cultures around the world, future research adopting grounded, bottom-up approaches will likely broaden the set of taxonomy dimensions and provide contextual adaptations to the item banks (Byrne, 2016; Hedrih, 2020). Another question being debated in well-being science relates to whether dimensions of positive mental health should be considered universal (i.e. relevant to all people, regardless of culture, context, or individual differences) or pluralist (i.e., important for some dependent on individual factors) (Fowers et al., 2023). While this discourse continues, it is likely that there is some middle point whereby many dimensions are relevant for most people and cultures, with others being more specific (Vaillant, 2012). We argue that grounded, bottom-up approaches, along with top-down methods are required to understand the degree of universality of each of the dimensions in the taxonomy, offering more detailed understanding of the contexts in which these dimensions are valued across many domains such as culture, gender, lived experience of mental illness, and life stage. The justification for starting the item pools from existing measures of positive mental health was discussed in Iasiello et al. (2024), and the authors have avoided ‘editorial’ decisions when selecting the dimensions. We anticipate that many researchers may disagree with some (or many) of the included dimensions. For example, on first glance, coping may not seem part of positive mental health, although it is mentioned explicitly in the World Health Organization (WHO) definition (WHO, 2001). It could be argued that coping processes directly influence how individuals maintain or regain mental health under stress, and therefore warrant inclusion in any comprehensive taxonomy of positive mental health. A Delphi study is currently in preparation, which presented the current dimensions to experts from a range of disciplines, aimed at continuing these discussions and reaching academic consensus on the included dimensions. Finally, item banks were calibrated in a single sample, which we do not intend to provide a conclusive definition of positive mental health. There was some evidence of over-fitting, potentially indicating that there was insufficient variability in the sample or items to test for fit. In our attempt to standardize items for scales, we had to make decisions on timeframes, response patterns, and tense; all of which may have impacted the psychometric properties. While we drew on existing evidence in making these psychometric decisions, in the absence of clear recommendations, the impact of our choices is largely unknown. For example, we found that several items violated local independence, which assumes that conditional on the latent variable(s), the responses to items are independent of each other. For example, the commonly used item ‘I feel satisfied with my life’ item demonstrated local dependence within the life satisfaction item pool, indicating that it correlated too highly with other items in the latent variable. As a result, this item was excluded from the final item bank; however, its local dependence may indicate its strength as a single item in measuring a higher-dimension construct of positive mental health. Prior research suggests that using a single life satisfaction item is recommended for measuring well-being when space is limited on a survey (VanderWeele et al., 2020). For this reason, it is important that local dependence is not used to infer poor item candidature in single-item measures but instead underscores the importance of testing assumptions and item selection for specific academic purposes (i.e. whether measuring a latent variable or epidemiological research using single items) and the need for replication in diverse populations. Further testing that varies timeframes and response scales, for instance, will provide further insight into the specific impact these choices make on the psychometric properties of a measure, specifically within the context of positive mental health measurement. Further testing should include testing external and predictive validity, and qualitative methodologies such as cognitive interviewing, which involves investigating the perceived meaning and clarity of time frames, response options, and item content (Castillo-Díaz & Padilla, 2013). Conclusion The current study developed a multi-dimensional measure of positive mental health. We produced and validated item banks for 26 dimensions of positive mental health with the intent of clarifying conceptual clarity, improving measurement precision, and reducing the jingle-jangle problem in well-being science. This work offers researchers and clinicians vetted tools that provide much-needed consistency in positive mental health assessment. Measurement lies at the foundation of well-being science; our hope is that this work improves the quality of well-being science. Methods This study extends prior research conducted by Iasiello and colleagues (2024) which synthesized the Level 3 dimensions of 155 measures of positive mental health, into a set of 21 general dimensions of positive mental health (e.g., happiness, meaning and purpose, optimism). The current study aimed to develop item banks to enable consistency in assessment across each of the Iasiello et al., (2024) dimensions of positive mental health, to help reduce the unhelpful redundancy, particularly at levels 3 and 4 described above. The methods of the current study were pre-registered and described on the Open Science Framework (OSF; https://osf.io/fs7uk/). The procedure generally following the main stages of the Patient Reported Outcomes Measurement Information System (PROMIS) protocol (Cella et al., 2010; DeWalt et al., 2007). The procedure involved four steps: (1) construction of the item pools, (2) Reduction of the item pools, (3) Standardization and selection of items for testing, (4) calibration of the item pools samples from the US and Australia. An item pool is defined as a collection of test items designed to measure one or more psychological constructs. Please note that item pools refer to untested lists of items, whereas item banks refer to calibrated item pools. Step 1: Construction of the item pools The current study started with 21 item pools for each of the positive mental health dimensions identified in Iasiello et al. (2024). The items were extracted from 149 of the identified 155 measures (n=6 measures were excluded as either commercially protected or could not be found). This resulted in a total of n=3,555 items which were allocated into the 21 dimensions. For details on the allocation of the items into the various item pools please see Supplementary Materials Table 1. A 22 nd item pool was constructed to hold all of the items which came from original measures that did not include dimensions (i.e. measured general well-being). These items were reviewed by three authors (XX, XXX, XX) to assess whether items appeared appropriate for the item pool, and re-sorted where it was considered that the items better fit the item pool of a different dimension. This was necessary for several reasons, for example, some items fit the dimensions poorly, some scales used filler items which were not appropriate for the item pool, and some items crossed multiple dimensions. Throughout this process, authors identified ‘subdimensions’ of items within each dimension item pool, for example the dimension of Autonomy had items that were thematically distinct, falling under either ‘Control over decisions or behaviors’ or ‘Expression of self’). Two authors (XX, XXX) reviewed the items of the 22 nd general item pool, identifying 5 additional dimensions which were not represented in the original 21. These were Control over Life, Achievement, Fun, Curiosity, and Other Focus, and were treated as subdimensions of an uncategorized dimension (Supplementary Materials Table S1). Step 2: Reduction of the item pools Following the PROMIS protocol and predetermined criteria for the study, items in each of item pools were removed using the following criteria: 1) item content was inconsistent with the dimension definition (Table S1); 2) an item was too similar (or semantically redundant) to another item in the pool; 3) the item content was too narrow to have universal applicability; 4) the item was disease specific, reducing general applicability of the item; and (5) the item was confusing (DeWalt et al., 2007). As there were often >100 items per pool, an intermediate step was included to assist with the identification of semantically redundant or duplicate items. Authors grouped similar items within the item pools into their subdimensions (Table S1). Step 3: Item standardization and selection of items for testing To reduce the number of items into a sample that could be feasibly tested in the current study, the author team voted on the remaining items in each of the 26 item pools (21 dimensions identified by Iasiello et al. (2024), plus the additional five dimensions aforementioned). The authors voted on four items per subdimension with the greatest face-validity. The items with the highest votes were standardized to include a consistent tense (past-tense), timeframe (two weeks), valence (positive wording), and bipolar response scale (1 = Strongly disagree , 2 = Disagree , 3 = Somewhat disagree, 4 = Neutral , 5 = Somewhat agree , 6 = Agree , 7 = Strongly Agree) (consistent with recommendations by (Clark & Watson, 2019; Diener et al., 1991; Simms et al., 2019). Step 4: Calibration of the item pools Participants The study received ethical approval from The XXXX University Human Research Ethics Committee (5331). Respondents were recruited via Qualtrics Research Services, who provide professional participant recruitment services to researchers seeking representative samples. The sample included adults aged 18+ with representation by age and sex across the United States of America (USA) and Australia (AUS). The survey was completed online using the Qualtrics platform. Respondents were excluded if they completed the survey too fast (i.e. less than 4 seconds per item), failed the attention check, were identified as a bot (Qualtrics, 2025), did not complete the survey, or were younger than 18 years old. To counter ordering effects, both the order of the positive mental health dimensions and the items within each of the dimensions were randomized. Participant recruitment continued until the sample surpassed 400 participants from the USA and AUS each, allowing for analysis to split the data into exploratory and confirmatory samples with adequate power (Fabrigar et al., 1999; Goretzko et al., 2021). Validation of the 26-Dimension Model Exploratory Structural Equation Modelling (ESEM) was used to confirm the factor structure of the 26-factor model. Exploratory Structural Equation Modelling (ESEM) was conducted, which allows for items to cross-load across all factors, leading to more accurate estimated factor intercorrelations (Joshanloo & Jovanović, 2017). For this reason, ESEM has been recommended as a more appropriate analytical technique compared to traditional Confirmatory Factor Analysis (CFA) in assessing the structure of multidimensional constructs such as positive mental health (Marsh et al., 2009). Modelling was conducted in Mplus (Muthén & Muthén, 2017). Using the full sample of participants, the 26-factor model was first estimated using CFA, based on the preference for a more parsimonious model (Marsh et al., 2004). An ESEM model was subsequently created and compared with the more parsimonious CFA model. Relative model fit (i.e., a comparison of model fit indices) and analysis of absolute fit indices was used to assess the models. We used the following criteria to gauge model fit, root mean square error of approximation (RMSEA) .95; and standardized root mean square residual (SRMR) < .08 (Hu & Bentler, 1999; Ng et al., 2017). Calibration of Item Banks Calibrating item banks using IRT requires several assumptions of the item pool to be met (Batterham et al., 2016). The first assumption is that latent variables are unidimensional, which was tested in a randomly selected half of the sample using Confirmatory Factor Analysis with maximum likelihood parameter estimation and Santorra-Bentler corrections for each of the identified dimensions. Where insufficient model fit was observed, exploratory factor analysis (EFA) was used to investigate the factor structure within the dimension using the second half of the sample. Exploratory Factor Analysis used a maximum likelihood estimator with equamax rotation (Schmitt & Sass, 2011), and decisions of factor retention were informed by multiple criteria including Parallel Analysis and the Velicer’s minimum average partial (MAP) test (Goretzko et al., 2021). Acceptable EFA model fit of RMSEA < .08 was used. EFA models were again confirmed with CFA in the original sample. Both CFA and EFA were conducted using the Lavaan package in R (Rosseel, 2012). Analyses that include multiple latent attributes (e.g., subscales) are called multidimensional IRT (MIRT) models. We used MIRT models where CFA/EFA found multi-dimensional structure for each of the item pools using the MIRT R package (Chalmers, 2012) with the graded response model (GRM). The second assumption of IRT is that items demonstrate local independence, meaning that the items only correlate with each other through their shared relationship with the latent variable. Locally dependent items were identified by inspecting the modification indices pertaining to residual correlations of the items per each latent factor. Large modification indices associated with significant residual correlations >.3 between item pairs were inspected for content similarity. Items from each item pair with significantly high residual correlations were excluded from the final latent variable if it displayed similarity in content or poorer readability compared to the partner item or had residual correlations greater than >.3 with multiple items (Batterham et al., 2016). IRT analyses generally model the relation between a latent construct and participants’ response choices. The latent attribute (here called a dimension of positive mental health) is represented by the Greek letter Theta (θ), scaled with M = 0 and SD = 1 (Johnson et al., 2022). We considered the item-level characteristic item’s discrimination, which indicates an item’s ability to discriminate between persons who differ in their levels of θ. Discrimination, denoted by ‘a’, can range from 0 to positive infinity, where larger values indicate greater discrimination of the item. Declarations We confirm that participants consented to participate in the study, as per ethics committee approval. References Alexandrova, A., & Fabian, M. (2022). Democratising Measurement: or Why Thick Concepts Call for Coproduction. European Journal for Philosophy of Science , 12 (1), 7. https://doi.org/10.1007/s13194-021-00437-7 Allardt, E. (1976). Dimensions of Welfare in a Comparative Scandinavian Study. Acta Sociologica , 19 (3), 227-239. http://www.jstor.org/stable/4194131 Altgassen, E., Geiger, M., & Wilhelm, O. (2024). Do you mind a closer look? A jingle-jangle fallacy perspective on mindfulness. 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Psychiatry Res , 317 , 114883. https://doi.org/10.1016/j.psychres.2022.114883 Table Table 1 Flow of Item Allocation and Item Reduction Original buckets Kappa After reallocation Removed in winnowing Reviewed by team Final to be tested Activities and functioning 137 0.67 243 221 22 12 Approach coping 284 0.76 Emotion-focused coping 145 173 106 67 20 Problem-focused coping 139 131 96 35 8 Autonomy 70 0.77 65 43 22 8 Avoidant coping 88 0.68 59 19 40 16 Calmness 25 0.72 93 81 12 4 Competence 139 0.71 133 82 51 16 Development 69 0.71 32 12 20 4 Engagement 21 0.81 14 4 10 4 Happiness 152 0.67 55 42 13 4 Life Satisfaction 70 0.74 73 58 15 4 Meaning and purpose 107 0.81 86 67 19 8 Optimism 50 0.58 61 36 25 8 Personal circumstances 193 0.7 134 128 6 4 Personal relationships 335 0.84 376 289 87 24 Physical Health 187 0.9 274 258 16 8 Self-acceptance 71 0.76 116 79 37 12 Self-congruence 60 0.55 49 32 17 4 Sense of community 56 0.64 57 23 34 16 Spirituality 127 0.8 100 53 47 16 Uncategorized 661 0.71 104 64 40 20 Vitality 35 0.77 90 66 24 8 General buckets Coping 17 0.71 Mental well-being 287 0.74 Psychological distress 114 0.79 Quality of life 200 0.9 Total 3555 Indiscriminate 515 Unclear 522 Table 2 Participant Characteristics Total Sample AUS (n = 413) USA (n = 424) Age 18-24 116 (13.9%) 51 (12.3%) 65 (15.3%) 25-34 150 (17.9%) 76 (18.4%) 74 (17.5%) 35-44 131 (15.7%) 71 (17.2%) 60 (14.2%) 45-54 138 (16.5%) 72 (17.4%) 66 (15.6%) 55-64 140 (16.7%) 62 (15.0%) 78 (18.4%) 65+ 162 (19.4%) 81 (19.6%) 81 (19.1%) Sex Male 401 (47.9%) 196 (47.5%) 205 (48.3%) Female 428 (51.1%) 214 (51.8%) 214 (50.5%) Prefer to self-describe 5 (0.6%) 2 (0.5%) 3 (0.7%) Prefer not to answer 3 (0.4%) 1 (0.2%) 2 (0.5%) Education Junior secondary 39 (4.7%) 26 (6.3%) 13 (3.1%) Senior secondary 229 (27.4%) 167 (40.4%) 62 (14.6%) Diploma 280 (33.5%) 92 (22.3%) 188 (44.3%) Bachelor’s degree 208 (24.9%) 98 (23.7%) 110 (25.9%) Master’s degree 63 (7.5%) 26 (6.3%) 37 (8.7%) Doctoral degree 18 (2.2%) 4 (1.0%) 14 (3.3%) Ethnicity White/European 619 (74.0%) 304 (73.6%) 315 (74.3%) Aboriginal/Torres Strait Islander 17 (2.0%) 16 (3.9%) 1 (0.2%) Asian/Indian 63 (7.5%) 46 (11.1%) 17 (4.0%) African 5 (0.6%) 4 (1.0%) 1 (0.2%) African American 49 (5.9%) 0 (0.0%) 49 (11.6%) Latin American 22 (2.6%) 4 (1.0%) 18 (4.2%) Other 48 (5.7%) 29 (7.0%) 19 (4.5%) Prefer not to answer 14 (1.7%) 10 (2.4%) 4 (0.9%) Mental health diagnosis Yes 293 (35.0%) 155 (37.5%) 138 (32.5%) No 544 (65.0%) 258 (62.5%) 286 (67.5%) Table 3 Factor Analysis fit of Within-Dimension Testing Proposed model Exploratory Model CFA RMSEA TLI CFI # factors RMSEA TLI CFI Activities and Functioning .103 .929 .942 2 .089 .955 .965 Autonomy .153 .897 .926 2 .023 .996 .997 Avoidant coping .112 .864 .882 3 .053 .969 .974 Calmness 0 1 1 1 Competence .049 .962 .967 1 Development 0 1 1 1 Emotion-focused .122 .788 .810 4 .055 .956 .962 Engagement .096 .938 .979 1 Happiness 0 1 1 1 Life Satisfaction .224 .915 .972 1 Meaning and Purpose .072 .985 .989 1 Optimism .116 .959 .971 2 .079 .981 .987 Personal Circumstances .088 .972 .991 1 Personal relationships .091 .900 .909 4 .069 .943 .949 Physical health .169 .839 .885 3 .037 .992 .995 Problem-focused .098 .920 .943 1 Self-acceptance .169 .839 .885 3 .015 .999 .999 Self-congruence .128 .961 .987 1 Sense of community .127 .853 .873 3 .08 .942 .951 Spirituality .148 .846 .867 2 .085 .959 .966 Vitality .022 .998 .999 1 Undefined Achievement .037 .999 .997 1 Control over life .430 .983 .994 1 Curiosity .104 .946 .982 1 Fun .131 .940 .980 1 Other focus .000 1.0 1.0 1 Additional Declarations There is NO Competing Interest. 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Batterham","email":"","orcid":"","institution":"Australian National University","correspondingAuthor":false,"prefix":"","firstName":"Philip","middleName":"J.","lastName":"Batterham","suffix":""},{"id":469171201,"identity":"779da317-ae3c-4882-81ef-84204c873a7b","order_by":6,"name":"Fallon Goodman","email":"","orcid":"","institution":"George Washington University","correspondingAuthor":false,"prefix":"","firstName":"Fallon","middleName":"","lastName":"Goodman","suffix":""},{"id":469171202,"identity":"44cafb47-3df9-4e52-a028-68d0434eac65","order_by":7,"name":"Aaron Jarden","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Aaron","middleName":"","lastName":"Jarden","suffix":""},{"id":469171203,"identity":"973c7629-922e-4c3b-bff0-481c41dc9451","order_by":8,"name":"Todd Kashdan","email":"","orcid":"","institution":"George Mason University","correspondingAuthor":false,"prefix":"","firstName":"Todd","middleName":"","lastName":"Kashdan","suffix":""},{"id":469171204,"identity":"c5e3bf80-ab5b-4e13-8173-2592cedc75f9","order_by":9,"name":"Michael Kyrios","email":"","orcid":"","institution":"Flinders University","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Kyrios","suffix":""},{"id":469171205,"identity":"b9a5fbec-d545-4843-b65a-4533df87a8ef","order_by":10,"name":"Lindsay Oades","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Lindsay","middleName":"","lastName":"Oades","suffix":""},{"id":469171206,"identity":"cc3a1817-0797-4289-b87a-bdf6524e96b0","order_by":11,"name":"Dorota Weziak-Bialowolska","email":"","orcid":"https://orcid.org/0000-0003-2711-2283","institution":"Kozminski University","correspondingAuthor":false,"prefix":"","firstName":"Dorota","middleName":"","lastName":"Weziak-Bialowolska","suffix":""},{"id":469171207,"identity":"fba7bdce-3bb4-4145-8798-4af718c9e7fa","order_by":12,"name":"Daniel Fassnacht","email":"","orcid":"https://orcid.org/0000-0001-6542-5008","institution":"University of the Sunshine Coast","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Fassnacht","suffix":""}],"badges":[],"createdAt":"2025-06-10 10:56:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6862291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6862291/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84905191,"identity":"71f73212-4ca0-4521-ae23-880d06286af2","added_by":"auto","created_at":"2025-06-18 15:45:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65707,"visible":true,"origin":"","legend":"\u003cp\u003eSTROBE Statement\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6862291/v1/2b40cc9c4de79a8968d1af5d.png"},{"id":85213239,"identity":"01535cd8-eb89-4bee-9e96-045d562cf9df","added_by":"auto","created_at":"2025-06-23 12:53:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1034230,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6862291/v1/69e7870d-0257-416d-b25e-d6af2585c22d.pdf"},{"id":84905190,"identity":"0fd42b29-f3d6-4575-84f2-ef6dc6ddaefd","added_by":"auto","created_at":"2025-06-18 15:45:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":107432,"visible":true,"origin":"","legend":"Supplementary Materials","description":"","filename":"NMHIasielloSUPP.docx","url":"https://assets-eu.researchsquare.com/files/rs-6862291/v1/c501b9e010970efd36c62110.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"\u003cp\u003eMeasuring well-being: Creating a taxonomy and item bank for positive mental health\u003c/p\u003e","fulltext":[{"header":"Public Significance Statement","content":"\u003cp\u003eUnderstanding and promoting positive mental health is crucial for enhancing individual and societal well-being. However, the field faces challenges due to inconsistent terminology and measurement. This study addresses these issues by creating a comprehensive set of item banks across 26 dimensions of positive mental health and tested them across American and Australian populations. This work aims to produce reliable tools for researchers, practitioners, and policymakers, thereby fostering better research, policy, and public understanding of positive mental health.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eWell-being science is a field aimed at understanding the full spectrum of human states and functioning from languishing to flourishing (Huppert, 2014; Keyes, 2002). For clarity, we refer to ‘positive states and functioning’ as \u003cem\u003epositive mental health\u003c/em\u003e, a term that is often synonymous with mental well-being and more easily distinguished from mental illness.\u0026nbsp;Possibly explained by the exponential growth in the field\u0026nbsp;(Wang et al., 2023), confusion remains about how positive mental health should be defined and measured\u0026nbsp;(Martela, 2024; VanderWeele et al., 2020). There is an ever-growing list of frameworks and measurement tools of positive mental health, and it is imperative that the field begins to distinguish between useful variety and unhelpful redundancy. The diversity in the field exists at four levels: (1) different theoretical views about what positive mental health is\u0026nbsp;(Robbins \u0026amp; Friedman, 2018)\u003cem\u003e,\u0026nbsp;\u003c/em\u003e(2)the dimensions which constitute positive mental health\u0026nbsp;(Martela \u0026amp; Sheldon, 2019; Novak et al., 2025), (3) inconsistent terminology to describe the dimensions\u0026nbsp;(van Zyl \u0026amp; Rothmann, 2022), and (4) inconsistent measurement of the dimensions\u0026nbsp;(Iasiello et al., 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDifferences in theoretical views by which positive mental health can be considered are a natural and vital source of variety, as models have been developed within specific disciplines (i.e., economics vs psychology; Edmunds, 2010), with contrasting philosophical views (i.e., desire satisfaction vs mental state; Woodard, 2015). To date, it has been largely illuminating for the academic community to have research streams dedicated to each of these views, contributing different understandings of human nature (Allardt, 1976; Martela \u0026amp; Sheldon, 2019). However, confusion occurs when we merely label each of these as ‘well-being’ and do not differentiate between them. For example, the abstracts of two intervention studies might each report improvements in a sample’s positive mental health, only for reviewers to find that the improvements were in satisfaction with life in the first study, and agency and a sense of belonging in the second: blurring (in this case) the lines between hedonic and eudemonic well-being.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe majority of models and frameworks of positive mental health are multi-dimensional, mental state models, which describe lists of dimensions that constitute positive mental health (Woodard, 2015). Again, there are appropriate reasons for diversity in these lists of dimensions, for example models developed for different cultural settings (Lomas, 2021) or for particular ages, demographics, or social groups (Alexandrova \u0026amp; Fabian, 2022). Unhelpful redundancy occurs when we maintain a range of partially-overlapping models of positive mental health, which are used interchangeably in practice. A review of measures of flourishing stated that “current dissensus [of dimensions] undermines the credibility of flourishing research and decreases the coherence and cumulation of the research domain” (Novak et al., 2025, p. 73).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese often-arbitrary differences in combinations of dimensions are compounded by differences in terminology at the dimensional level, representing the third level of unhelpful redundancy \u0026nbsp;(Fowers et al., 2024). Iasiello et al. (2024) reviewed measures of mental well-being, quality of life, and resilience/coping, finding 155 measures of positive mental health with more than 400 dimensions described by the measures. Despite differences in language and nomenclature, the list could be thematically synthesized into a coherent set of 21 dimensions. For example, some measures were said to measure Happiness as a dimension of positive mental health while others said to measure Positive Affect, while the items used to measure the dimensions were effectively identical.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, the way these dimensions are operationalized into measures can substantially differ. For example, Novak et al. (2025) compared the affective well-being items of flourishing measures, finding items such as “In general, how often do you feel positive?”, “‘I feel happy most of the time’”, and “In general, how happy or unhappy do you usually feel?”. Iasiello et al. (2024) found that the structure of well-being measures can differ considerably in Likert scale options, response formats and timeframe. The practical impact of these differences are poorly understood, and likely make comparisons between measures inappropriate (Hone et al., 2014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConfusion at these four levels exacerbates the “jingle-jangle” problem within well-being science (Disabato et al., 2025; Marsh et al., 2019), which has previously been attributed to vague connection between psychological theory and its operationalization in empirical studies (Hanfstingl et al., 2024). The jingle-jangle problem refers to a semantic issue where different terms are used to describe the same concept (jingle), or the same term is used for different concepts (jangle). For instance, “jingle” occurs when two researchers use different names for what is essentially the same psychological construct (e.g., curiosity, openness, and love for learning are treated as distinct in Peterson \u0026amp; Seligman, 2004). A “jangle” occurs when the same name is applied to difference concepts, such as positive affect being used indeterminately to refer to both ‘activated’ states such as joy and happiness or with ‘low-activation’ states such as calmness and serenity (LaRowe et al., 2024). Jingle-jangle fallacies have been discussed across well-being science, in joy (Schnitker et al., 2020), \u0026nbsp;mindfulness (Altgassen et al., 2024), empathy (Ayache et al., 2024), and flourishing (van Zyl \u0026amp; Rothmann, 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Current Study\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClear terminology and precise, comparable, and comprehensive measurement tools are needed for the potential of well-being science to be realized by clinicians, policymakers, educators, and employers (Guðmundsdóttir, 2011; VanderWeele, 2017). This issue of conceptual ambiguity is neither new nor unique to the measurement of positive mental health (Pilkonis et al., 2011). Item banks have been used to solve similar terminology and measurement issues in areas across several health domains (Cella et al., 2019) and mental illness (Batterham et al., 2016). This study aims to develop and calibrate item banks using the measures and dimensions of positive mental health identified by Iasiello et al., (2024), resulting in brief measurement tools to enable consistency in assessment across multiple dimensions of positive mental health.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eItem Pool Construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe flow of items into pools, reallocation, winnowing, and selection is displayed in Table 1. A total of 3,555 items were extracted, following reallocation by three authors (interrater kappa ranging from .55 to .90 depending on the dimension), 2,518 items were available for winnowing and 515 non-discriminative and 522 unclear items were removed from the pools. Winnowing resulted in a total of 659 items selected for inclusion, with a range of 6-87 items per dimension. The variation in item count was due to the differing number of measures found per each dimension. After ranking items for face validity, the final item pool resulted in 228 items selected for testing, with a range of 4-24 items per dimension, which was dependent on the number of subdimensions identified within each of the dimensions, see Supplementary Materials (Table S1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInsert Table 1 here\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of the Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe STROBE statement for the survey participants is depicted in Figure 1. Of the 2,192 participants who were assessed for eligibility, n = 837 were included in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInsert Figure 1 here\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe major reason for exclusion was failing the attention check (n = 513) and completing the survey too quickly (n = 406). Participant characteristics are detailed in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInsert Table 2 here\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactor structure of the model:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall CFA model demonstrated poor fit, 𝜒\u003csup\u003e2\u003c/sup\u003e(24205) = 57834.8, \u003cem\u003ep\u003c/em\u003e \u0026lt;.001, RMSEA = .041 CI\u003csub\u003e90%\u003c/sub\u003e [.040, .041], CFI = .825, TLI = .821, SRMR = .071. The ESEM model demonstrated acceptable fit, 𝜒\u003csup\u003e2\u003c/sup\u003e(19280) = 35905.0, \u003cem\u003ep\u003c/em\u003e \u0026lt;.001, RMSEA = .032 CI\u003csub\u003e90%\u003c/sub\u003e [.032, .033], CFI = .913, TLI = .889, SRMR = .014. Small-medium correlations were identified between the factors (as indicated in Supplementary Materials Table S2), with 6.7% (n = 22/325) of the factor pairs correlating \u0026gt; 0.5. Most of these medium sized factor pair correlations came from Self-acceptance (n = 9), Competence (n = 5), and Emotion-focused coping (n = 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWithin-Dimension Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndices of model fit for the initial models are provided in Table 3. Eight dimensions displayed a unidimensional structure with acceptable fit (i.e., Competence, Engagement, Life Satisfaction, Meaning and Purpose, Personal circumstances, Problem-focused coping, Self-congruence, and Vitality), while 10 were tested using exploratory analysis. There were three dimensions that showed evidence of over-fitting, based on RMSEA = 0 (i.e., Calmness, Development, and Happiness).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInsert Table 3 here\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe items, their IRT parameters, including discrimination (slopes) and difficulty (thresholds), for the resulting item banks are provided in Supplementary Materials (Table S3). Fifty-two items were removed based on local dependence.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study aimed to address the use of inconsistent terminology and overlapping constructs in measures of positive mental health. This is a barrier to academic communication and can derail progress in research and practice. In response, we developed calibrated item banks for 26 dimensions of positive mental health following established guidelines for item bank formation (Cella et al., 2010). This work offers researchers and clinicians brief, vetted tools that provide conceptual clarity and consistency in positive mental health assessment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 26 item banks were developed from existing, validated measurement tools in the literature. Construction of the item pools revealed many shortfalls in measures, which, unfortunately permeate well-being science. For instance, 29% of items were removed from the item pools due to being either indiscriminate (meaning they could be misinterpreted as two different dimensions) or unclear. Similarly, the process revealed a large degree of semantically redundant items that exist among the many measures of positive mental health, indicating that there are few unique items being used despite the many available measures. To illustrate, semantically redundant life satisfaction items included: “I am satisfied with my current life”, “I am satisfied with my life”, “I felt satisfied with my life”, and “I am very satisfied with my life”. These issues are important for the future of well-being science, particularly in cross-cultural or heterogenous samples (Kiknadze \u0026amp; Fowers, 2023), as the interpretation, and therefore value, of these items may vary considerably between individuals, affecting the psychometric validity of the measure (i.e., measurement invariance, Putnick \u0026amp; Bornstein, 2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;As demonstrated by exploratory structural equation modelling (ESEM), there was acceptable model fit for the 26-factor model. This finding should not be interpreted as the authors establishing a ‘new’ 26-factor model of positive mental health, but instead to support the ‘separability’ of the 26-dimensions. The fact that such a multi-dimensional model showed acceptable fit with minimal correlation between factors is an endorsement of the rigorous item pool construction process. By carefully sorting items, the resultant banks appear to have avoided a well-established issue in well-being science, that latent factors are often highly correlated with each other, even when using a less stringent technique such as ESEM (Joshanloo \u0026amp; Jovanović, 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs discussed, measurement dissensus undermines the credibility and reliability of well-being science. The results of the current study lay the foundation for a formal taxonomy of positive mental health, mirroring successful developments in other health areas suffering inconsistent language use and overlapping models (i.e. behavior change; \u0026nbsp;Michie et al., 2013).\u0026nbsp;It is anticipated that the set of dimensions and calibrated item banks (see Supplementary Materials Table S3) can facilitate greater consistency in mental health research and intervention and enable innovation across a range of areas in the field. However, like other successful efforts to establish a taxonomy, we consider that the results of this study represent a starting point, and that multiple versions with transparent testing and consultation are required (for example, Atkins et al., 2017). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to clarifying the literature and enabling consistency in well-being science, the major applications of the item banks relate primarily to a shift from ‘overall well-being’ to a dimensional view (Joseph \u0026amp; Wood, 2010).\u0026nbsp;For example, it has been demonstrated that positive mental health is a protective factor from future mental illness, and the absence of positive mental health exposes an individual to heightened risk (Burns et al., 2022; Keyes et al., 2010). However, it is not yet understood which dimensions (or combinations of dimensions) are most explanatory of this risk, or whether certain dimensions are more or less relevant for particular demographics. \u0026nbsp;High quality assessment of the dimensions identified in the current study would enable greater precision could further assist in identifying people who are languishing but may not yet have symptoms of a mental illness (Rottenberg et al., 2018). \u0026nbsp;Similarly, this separation of dimensions enables a greater understanding of intervention efficacy and mechanisms. While meta-analyses have demonstrated that overall well-being can be improved following interventions, much less is known about which interventions are effective at improving specific dimensions, and less again understood about the mechanisms by which effectiveness occurs (Schotanus-Dijkstra et al., 2019). This work need not be limited to psychological interventions, but also across all sociological levels, for example, where precise understanding of policies impacting specific dimensions of positive mental health would allow for more effective allocation of resources (Kinderman et al., 2015).\u003c/p\u003e\n\u003cp\u003eThese applications are also relevant for clinical research and practice (Goodman, 2025). Individuals rarely seek clinical treatment only to reduce mental health symptoms or distress; they also report a desire to improve aspects of positive mental health (Chevance et al., 2020; Rottenberg \u0026amp; Kashdan, 2022), and sometimes doing so is equally or more important to them than symptom reduction (Zimmerman et al., 2022). Therefore, to measure therapeutic success, interventionists need measurement tools to determine whether patients experienced reductions in illness symptoms \u003cem\u003eand\u003c/em\u003e improvements in well-being, i.e., complete mental health (Keyes, 2003). Sound measures of positive mental health can also provide insight about how well a person responds to a particular treatment. For example, people with depression often experience deficits in positive affect, but traditional treatment focuses on reducing negative affect and distress (Devendorf et al., 2022). Newer treatments that incorporate interventions to increase positive emotions have shown promise in both reducing depression symptoms and improving well-being (Craske et al., 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and Future Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study employed an empirically supported, data-driven approach to creating a taxonomy using systematic review principles based on published literature.\u0026nbsp;There are several limitations related to the sources of items, the construction of the item pools and the calibration of the item banks, which have flow on effects to the resultant taxonomy. Firstly, the item pools were constructed from existing measures of positive mental health, which are predominantly Western-oriented, and the item pools were calibrated in a Western sample. There is an ongoing debate in the literature regarding the relative value of expert (i.e., top-down) and lay (bottom-up) perspectives and definitions of positive mental health (Willen et al., 2025). While many of the source measures of positive mental health have been validated in many cultures around the world, future research adopting grounded, bottom-up approaches will likely broaden the set of taxonomy dimensions and provide contextual adaptations to the item banks (Byrne, 2016; Hedrih, 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother question being debated in well-being science relates to whether dimensions of positive mental health should be considered universal (i.e. relevant to all people, regardless of culture, context, or individual differences) or pluralist (i.e., important for some dependent on individual factors) (Fowers et al., 2023). While this discourse continues, it is likely that there is some middle point whereby many dimensions are relevant for most people and cultures, with others being more specific (Vaillant, 2012). We argue that grounded, bottom-up approaches, along with top-down methods are required to understand the degree of universality of each of the dimensions in the taxonomy, offering more detailed understanding of the contexts in which these dimensions are valued across many domains such as culture, gender, lived experience of mental illness, and life stage.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe justification for starting the item pools from existing measures of positive mental health was discussed in Iasiello et al. (2024), and the authors have avoided ‘editorial’ decisions when selecting the dimensions. We anticipate that many researchers may disagree with some (or many) of the included dimensions. For example, on first glance, coping may not seem part of positive mental health, although it is mentioned explicitly in the World Health Organization (WHO) definition (WHO, 2001). It could be argued that coping processes directly influence how individuals maintain or regain mental health under stress, and therefore warrant inclusion in any comprehensive taxonomy of positive mental health.\u0026nbsp;A Delphi study is currently in preparation, which presented the current dimensions to experts from a range of disciplines, aimed at continuing these discussions and reaching academic consensus on the included dimensions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, item banks were calibrated in a single sample, which we do not intend to provide a conclusive definition of positive mental health. There was some evidence of over-fitting, potentially indicating that there was insufficient variability in the sample or items to test for fit. In our attempt to standardize items for scales, we had to make decisions on timeframes, response patterns, and tense; all of which may have impacted the psychometric properties. While we drew on existing evidence in making these psychometric decisions, in the absence of clear recommendations, the impact of our choices is largely unknown. For example, we found that several items violated local independence, which assumes that conditional on the latent variable(s), the responses to items are independent of each other. For example, the commonly used item ‘I feel satisfied with my life’ item demonstrated local dependence within the life satisfaction item pool, indicating that it correlated too highly with other items in the latent variable. As a result, this item was excluded from the final item bank; however, its local dependence may indicate its \u003cem\u003estrength\u003c/em\u003e as a single item in measuring a higher-dimension construct of positive mental health. Prior research suggests that using a single life satisfaction item is recommended for measuring well-being when space is limited on a survey (VanderWeele et al., 2020). For this reason, it is important that local dependence is not used to infer poor item candidature in single-item measures but instead underscores the importance of testing assumptions and item selection for specific academic purposes (i.e. whether measuring a latent variable or epidemiological research using single items) and the need for replication in diverse populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther testing that varies timeframes and response scales, for instance, will provide further insight into the specific impact these choices make on the psychometric properties of a measure, specifically within the context of positive mental health measurement. Further testing should include testing external and predictive validity, and qualitative methodologies such as cognitive interviewing, which involves investigating the perceived meaning and clarity of time frames, response options, and item content (Castillo-Díaz \u0026amp; Padilla, 2013).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current study developed a multi-dimensional measure of positive mental health. We produced and validated item banks for 26 dimensions of positive mental health with the intent of clarifying conceptual clarity, improving measurement precision, and reducing the jingle-jangle problem in well-being science. This work offers researchers and clinicians vetted tools that provide much-needed consistency in positive mental health assessment. Measurement lies at the foundation of well-being science; our hope is that this work improves the quality of well-being science.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study extends prior research conducted by Iasiello and colleagues (2024) which synthesized the Level 3 dimensions of 155 measures of positive mental health, into a set of 21 general dimensions of positive mental health (e.g., happiness, meaning and purpose, optimism). The current study aimed to develop item banks to enable consistency in assessment across each of the Iasiello et al., (2024) dimensions of positive mental health, to help reduce the unhelpful redundancy, particularly at levels 3 and 4 described above.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe methods of the current study were pre-registered and described on the Open Science Framework (OSF; https://osf.io/fs7uk/). The procedure generally following the main stages of the Patient Reported Outcomes Measurement Information System (PROMIS) protocol \u0026nbsp;(Cella et al., 2010; DeWalt et al., 2007). The procedure involved four steps: (1) construction of the item pools, (2) Reduction of the item pools, (3) Standardization and selection of items for testing, (4) calibration of the item pools samples from the US and Australia. \u0026nbsp;An item pool is defined as a collection of test items designed to measure one or more psychological constructs. Please note that item pools refer to untested lists of items, whereas item \u003cem\u003ebanks\u003c/em\u003e refer to calibrated item pools.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStep 1: Construction of the item pools\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe current study started with 21 item pools for each of the positive mental health dimensions identified in Iasiello et al. (2024). The items were extracted from 149 of the identified 155 measures (n=6 measures were excluded as either commercially protected or could not be found). This resulted in a total of n=3,555 items which were allocated into the 21 dimensions. For details on the allocation of the items into the various item pools please see Supplementary Materials Table 1. A 22\u003csup\u003end\u003c/sup\u003e item pool was constructed to hold all of the items which came from original measures that did not include dimensions (i.e. measured general well-being).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;These items were reviewed by three authors (XX, XXX, XX) to assess whether items appeared appropriate for the item pool, and re-sorted where it was considered that the items better fit the item pool of a different dimension. This was necessary for several reasons, for example, some items fit the dimensions poorly, some scales used filler items which were not appropriate for the item pool, and some items crossed multiple dimensions. Throughout this process, authors identified \u0026lsquo;subdimensions\u0026rsquo; of items within each dimension item pool, for example the dimension of Autonomy had items that were thematically distinct, falling under either \u0026lsquo;Control over decisions or behaviors\u0026rsquo; or \u0026lsquo;Expression of self\u0026rsquo;). \u0026nbsp;Two authors (XX, XXX) reviewed the items of the 22\u003csup\u003end\u003c/sup\u003e general item pool, identifying 5 additional dimensions which were not represented in the original 21. These were Control over Life, Achievement, Fun, Curiosity, and Other Focus, and were treated as subdimensions of an uncategorized dimension (Supplementary Materials Table S1). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStep 2: Reduction of the item pools\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the PROMIS protocol and predetermined criteria for the study, items in each of item pools were removed using the following criteria: 1) item content was inconsistent with the dimension definition (Table S1); 2) an item was too similar (or semantically redundant) to another item in the pool; 3) the item content was too narrow to have universal applicability; 4) the item was disease specific, reducing general applicability of the item; and (5) the item was confusing (DeWalt et al., 2007).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs there were often \u0026gt;100 items per pool, an intermediate step was included to assist with the identification of semantically redundant or duplicate items. Authors grouped similar items within the item pools into their subdimensions (Table S1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStep 3: Item standardization and selection of items for testing\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo reduce the number of items into a sample that could be feasibly tested in the current study, the author team voted on the remaining items in each of the 26 item pools (21 dimensions identified by Iasiello et al. (2024), plus the additional five dimensions aforementioned). The authors voted on four items per subdimension with the greatest face-validity. The items with the highest votes were standardized to include a consistent tense (past-tense), timeframe (two weeks), valence (positive wording), and bipolar response scale (1 = Strongly disagree , 2 = Disagree , 3 = Somewhat disagree, 4 = Neutral , 5 = Somewhat agree , 6 = Agree , 7 = Strongly Agree) (consistent with recommendations by (Clark \u0026amp; Watson, 2019; Diener et al., 1991; Simms et al., 2019).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStep 4: Calibration of the item pools\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received ethical approval from The XXXX University Human Research Ethics Committee (5331). Respondents were recruited via Qualtrics Research Services, who provide professional participant recruitment services to researchers seeking representative samples. The sample included adults aged 18+ with representation by age and sex across the United States of America (USA) and Australia (AUS).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe survey was completed online using the Qualtrics platform. Respondents were excluded if they completed the survey too fast (i.e. less than 4 seconds per item), failed the attention check, were identified as a bot (Qualtrics, 2025), did not complete the survey, or were younger than 18 years old. To counter ordering effects, both the order of the positive mental health dimensions and the items within each of the dimensions were randomized. Participant recruitment continued until the sample surpassed 400 participants from the USA and AUS each, allowing for analysis to split the data into exploratory and confirmatory samples with adequate power (Fabrigar et al., 1999; Goretzko et al., 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eValidation of the 26-Dimension Model\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExploratory Structural Equation Modelling (ESEM) was used to confirm the factor structure of the 26-factor model. Exploratory Structural Equation Modelling (ESEM) was conducted, which allows for items to cross-load across all factors, leading to more accurate estimated factor intercorrelations (Joshanloo \u0026amp; Jovanović, 2017). For this reason, ESEM has been recommended as a more appropriate analytical technique compared to traditional Confirmatory Factor Analysis (CFA) in assessing the structure of multidimensional constructs such as positive mental health (Marsh et al., 2009). Modelling was conducted in Mplus (Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n, 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing the full sample of participants, the 26-factor model was first estimated using CFA, based on the preference for a more parsimonious model (Marsh et al., 2004). An ESEM model was subsequently created and compared with the more parsimonious CFA model. Relative model fit (i.e., a comparison of model fit indices) and analysis of absolute fit indices was used to assess the models. We used the following criteria to gauge model fit, root mean square error of approximation (RMSEA) \u0026lt; .06; both comparative fit index (CFI) and Tucker-Lewis index (TLI) \u0026gt; .95; and standardized root mean square residual (SRMR) \u0026lt; .08 (Hu \u0026amp; Bentler, 1999; Ng et al., 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCalibration of Item Banks\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCalibrating item banks using IRT requires several assumptions of the item pool to be met (Batterham et al., 2016). The first assumption is that latent variables are unidimensional, which was tested in a randomly selected half of the sample using Confirmatory Factor Analysis with maximum likelihood parameter estimation and Santorra-Bentler corrections for each of the identified dimensions. Where insufficient model fit was observed, exploratory factor analysis (EFA) was used to investigate the factor structure within the dimension using the second half of the sample. Exploratory Factor Analysis used a maximum likelihood estimator with equamax rotation (Schmitt \u0026amp; Sass, 2011), and decisions of factor retention were informed by multiple criteria including Parallel Analysis and the Velicer\u0026rsquo;s minimum average partial (MAP) test (Goretzko et al., 2021). Acceptable EFA model fit of RMSEA \u0026lt; .08 was used. EFA models were again confirmed with CFA in the original sample. Both CFA and EFA were conducted using the Lavaan package in R (Rosseel, 2012). Analyses that include multiple latent attributes (e.g., subscales) are called multidimensional IRT (MIRT) models. We used MIRT models where CFA/EFA found multi-dimensional structure for each of the item pools using the MIRT R package (Chalmers, 2012) with the graded response model (GRM).\u003c/p\u003e\n\u003cp\u003eThe second assumption of IRT is that items demonstrate local independence, meaning that the items only correlate with each other through their shared relationship with the latent variable. Locally dependent items were identified by inspecting the modification indices pertaining to residual correlations of the items per each latent factor. Large modification indices associated with significant residual correlations \u0026gt;.3 between item pairs were inspected for content similarity. Items from each item pair with significantly high residual correlations were excluded from the final latent variable if it displayed similarity in content or poorer readability compared to the partner item or had residual correlations greater than \u0026gt;.3 with multiple items (Batterham et al., 2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIRT analyses generally model the relation between a latent construct and participants\u0026rsquo; response choices. The latent attribute (here called a dimension of positive mental health) is represented by the Greek letter Theta (\u0026theta;), scaled with \u003cem\u003eM\u003c/em\u003e = 0 and\u003cem\u003e\u0026nbsp;SD\u003c/em\u003e = 1 (Johnson et al., 2022). We considered the item-level characteristic item\u0026rsquo;s discrimination, which indicates an item\u0026rsquo;s ability to discriminate between persons who differ in their levels of \u0026theta;. Discrimination, denoted by \u0026lsquo;a\u0026rsquo;, can range from 0 to positive infinity, where larger values indicate greater discrimination of the item.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eWe confirm that participants consented to participate in the study, as per ethics committee approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlexandrova, A., \u0026amp; Fabian, M. (2022). Democratising Measurement: or Why Thick Concepts Call for Coproduction. \u003cem\u003eEuropean Journal for Philosophy of Science\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(1), 7. https://doi.org/10.1007/s13194-021-00437-7 \u003c/li\u003e\n\u003cli\u003eAllardt, E. (1976). 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The relative importance of diagnostic specific and transdiagnostic factors in evaluating treatment outcome of depressed patients. \u003cem\u003ePsychiatry Res\u003c/em\u003e,\u003cem\u003e 317\u003c/em\u003e, 114883. https://doi.org/10.1016/j.psychres.2022.114883\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003eFlow of Item Allocation and Item Reduction\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"647\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eOriginal buckets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eKappa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eAfter reallocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eRemoved in winnowing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eReviewed by team\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eFinal to be tested\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eActivities and functioning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eApproach coping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp;Emotion-focused coping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eProblem-focused coping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eAutonomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eAvoidant coping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eCalmness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eCompetence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eDevelopment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eEngagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eHappiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eLife Satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eMeaning and purpose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eOptimism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003ePersonal circumstances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003ePersonal relationships\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003ePhysical Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eSelf-acceptance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eSelf-congruence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eSense of community\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eSpirituality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eUncategorized\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eVitality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eGeneral buckets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Coping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Mental well-being\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Psychological distress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Quality of life\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e3555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eIndiscriminate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cem\u003eParticipant Characteristics\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"541\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eTotal Sample\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAUS (n = 413)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eUSA (n = 424)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e18-24\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e116 (13.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e51 (12.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e65 (15.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e25-34\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e150 (17.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e76 (18.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e74 (17.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e35-44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e131 (15.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e71 (17.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e60 (14.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e45-54\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e138 (16.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e72 (17.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e66 (15.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e55-64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e140 (16.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e62 (15.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e78 (18.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e65+\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e162 (19.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e81 (19.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e81 (19.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eSex\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e401 (47.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e196 (47.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e205 (48.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e428 (51.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e214 (51.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e214 (50.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003ePrefer to self-describe\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e5 (0.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2 (0.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3 (0.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003ePrefer not to answer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e3 (0.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1 (0.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e2 (0.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eEducation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eJunior secondary\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e39 (4.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e26 (6.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e13 (3.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eSenior secondary\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e229 (27.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e167 (40.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e62 (14.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eDiploma\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e280 (33.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e92 (22.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e188 (44.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eBachelor\u0026rsquo;s degree\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e208 (24.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e98 (23.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e110 (25.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eMaster\u0026rsquo;s degree\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e63 (7.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e26 (6.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e37 (8.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eDoctoral degree\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e18 (2.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4 (1.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e14 (3.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eEthnicity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eWhite/European\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e619 (74.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e304 (73.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e315 (74.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eAboriginal/Torres Strait Islander\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e17 (2.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e16 (3.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1 (0.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eAsian/Indian\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e63 (7.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e46 (11.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e17 (4.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eAfrican\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e5 (0.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4 (1.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1 (0.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eAfrican American\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e49 (5.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0 (0.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e49 (11.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eLatin American\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e22 (2.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4 (1.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e18 (4.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eOther\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e48 (5.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e29 (7.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e19 (4.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003ePrefer not to answer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e14 (1.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e10 (2.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e4 (0.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eMental health diagnosis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e293 (35.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e155 (37.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e138 (32.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e544 (65.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e258 (62.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e286 (67.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cem\u003eFactor Analysis fit of Within-Dimension Testing\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003eProposed model\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 254px;\"\u003e\n \u003cp\u003eExploratory Model CFA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e# factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eActivities and Functioning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.965\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eAutonomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eAvoidant coping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.974\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eCalmness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eCompetence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eDevelopment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eEmotion-focused\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eEngagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eHappiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eLife Satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eMeaning and Purpose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eOptimism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003ePersonal Circumstances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003ePersonal relationships\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003ePhysical health\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.995\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eProblem-focused\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eSelf-acceptance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eSelf-congruence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eSense of community\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eSpirituality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eVitality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eUndefined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Achievement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Control over life\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Curiosity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Fun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Other focus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"positive mental health, well-being, mental well-being, assessment, quality of life, resilience","lastPublishedDoi":"10.21203/rs.3.rs-6862291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6862291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe science of well-being has advanced rapidly, yet the field remains fragmented by inconsistent terminology and overlapping constructs; a challenge known as the “jingle-jangle” problem. \u0026nbsp;To address this, we developed and calibrated item banks for 26 dimensions of positive mental health, aiming to provide a comprehensive and precise framework for assessment. Drawing from a pool of 3,555 items sources from existing scales identified in a prior review (Iasiello et al., 2024), we refined the selection to 224 items through rigorous content evaluation. These items were then calibrated using factor analysis, structural equation modeling, and item response theory in a diverse sample of American and Australian adults (\u003cem\u003en\u003c/em\u003e=837). Findings supported the distinctiveness and separability of the 26 dimensions, enabling the creation of brief, precise measures. This multidimensional approach facilitates clearer communication between researchers, practitioners, and policymakers, enhancing the generalizability and replicability of well-being research and lays a foundation for a future taxonomy of positive mental health.\u003c/p\u003e","manuscriptTitle":"Measuring well-being: Creating a taxonomy and item bank for positive mental health","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 15:45:10","doi":"10.21203/rs.3.rs-6862291/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8f38591e-f3ca-48af-9b45-7eb29a31e6ed","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50253203,"name":"Health sciences/Risk factors"},{"id":50253204,"name":"Biological sciences/Psychology"}],"tags":[],"updatedAt":"2025-06-23T12:45:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 15:45:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6862291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6862291","identity":"rs-6862291","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}