Exposure to health misinformation, self-care confidence and delayed care-seeking among adults in the UK: a cross-sectional survey

Exposure to health misinformation, self-care confidence and delayed care-seeking among adults in the UK: a cross-sectional survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exposure to health misinformation, self-care confidence and delayed care-seeking among adults in the UK: a cross-sectional survey Austen El-Osta, Sami Altalib, Dumile Gumede, Saja Alnahar, Savid Skinner, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8824194/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 Objectives Generate population-level evidence on exposure to misleading health information and its behavioural implications within contemporary self-care. Design Cross-sectional, self-administered online survey. Setting United Kingdom. Participants 1,414 adults (mean age 46.8 years) recruited via a closed online panel using quota sampling by age, gender and ethnicity. Main outcome measures Self-reported exposure to false or misleading health information; perceived ability to recognise misinformation; delays in professional care-seeking following exposure. Secondary measures included self-care practices, trust in information sources and recognition of common misinformation tactics. Multivariable ordinal logistic regression examined predictors of recognition ability and care-seeking delays; Spearman correlations assessed associations between numeracy and recognition of misinformation tactics. Results Exposure to misleading health information was widespread: 81.8% of respondents reported encountering false or misleading content in the previous six months. Confidence in recognising misinformation was high overall, with strong educational gradients; university and postgraduate education were independently associated with higher perceived recognition ability. However, educational attainment did not protect against behavioural consequences. Exposure to misleading content showed a clear dose-response association to professional care-seeking, even after adjustment for age, sex, education, ethnicity and urbanicity. Compared with no exposure, odds of delay increased among those exposed once or twice (aOR 1.53), monthly (aOR 1.83) and weekly (aOR 1.89). Asian and Black participants had significantly higher odds of delay compared with White participants and urban residents had higher risk than peri-urban residents. Numeracy skills were positively associated with recognising sensational headlines and cherry-picked evidence, but not with detection of AI-generated or impersonated content. Conclusions Pharmaceutical and health misinformation was widespread and linked to inappropriate care-seeking. Although higher education increased perceived recognition, it did not eliminate behavioural risk. Traditional health literacy skills helped identify some forms of misinformation but offered limited protection against AI-enabled deception. These findings position pharmaceutical misinformation as a patient safety and access issue requiring coordinated, equity-sensitive, system-level responses alongside individual literacy efforts. Health Policy Pharmaceutical misinformation health misinformation self-care health literacy eHealth literacy care-seeking behaviour patient safety trust in health information artificial intelligence health equity Figures Figure 1 Figure 2 Summary box What is already known on this topic Health and pharmaceutical misinformation is widely encountered online and could influence health beliefs, intentions and self-care practices. Higher health literacy and educational attainment are associated with better recognition of misleading health information. Existing evidence focuses on vaccines or pandemic contexts, with less attention to medicines, self-care and routine health decision-making among the general population. What this study adds In a representative UK sample, exposure to false or misleading pharmaceutical and health information was near-universal, despite high trust in professional health sources and strong self-care confidence. Exposure to misleading health content was associated with delayed professional care-seeking in a clear dose-response pattern, even after adjustment for education and sociodemographic factors. Higher educational attainment increased perceived self-efficacy to recognise misinformation but did not protect against behavioural consequences such as care delays. Numeracy skills were associated with recognition of sensational framing and cherry-picked evidence, but not with detection of AI-generated or impersonated content. Socioeconomic-based variations were observed, with Asian and Black participants and urban residents exhibiting a higher risk of delayed care following exposure. How this study might affect research, practice or policy Health and medicines misinformation should be considered a patient safety and access issue, not solely a communication and promotion challenge. Interventions focused only on individual health literacy are unlikely to be sufficient; trust-building, transparency and professional mediation remain critical. Health systems and regulators should account for emerging AI-enabled misinformation tactics that are poorly addressed by traditional health literacy approaches. Targeted, culturally sensitive and patient centred strategies are needed to reduce misinformation-related harms in marginalised populations. Introduction The ability to access, interpret and act on health information is fundamental to effective self-care and to the timely use of healthcare services. Across high-income health systems, self-care is increasingly framed as both an individual capability and a system-level necessity, enabling people to manage minor ailments, monitor long-term conditions and make informed decisions about when professional care is needed ( 1 – 3 ). In the United Kingdom (UK), national policy and National Health Service (NHS) guidance actively promote self-management, supported by clear pathways for escalation to professional services. Yet the information environment in which these decisions are made has become increasingly complex, fragmented and contested ( 4 , 5 ). Over the past decade, the volume and velocity of online health information has expanded dramatically. Social media platforms, messaging services, search engines and generative artificial intelligence (AI) tools now coexist alongside traditional professional and institutional sources ( 6 , 7 ). While this diversification has improved access to health knowledge, it has also increased exposure to misleading, inaccurate or deliberately deceptive health information - often referred to as health misinformation or disinformation ( 8 , 9 ). The World Health Organisation (WHO) has characterised this phenomenon as a global “infodemic”, warning that false or misleading health claims can undermine public trust, distort risk perceptions and generate harmful behaviours at scale ( 10 , 11 ). Available literature has documented widespread exposure to health misinformation across diverse topics, including nutrition, vaccines, chronic disease “cures”, mental health and weight management ( 12 – 15 ). Importantly, misinformation does not operate solely by persuading individuals to adopt false beliefs but can also create uncertainty, confusion and decisional fatigue and paralysis. Experimental and observational studies suggest that exposure to conflicting or low-quality information might reduce confidence in health decisions, erode trust in healthcare institutions and delay engagement with healthcare services even among individuals who do not explicitly endorse false claims ( 16 – 20 ). Health literacy has frequently been proposed as a protective factor against misinformation ( 21 – 23 ). Conceptually, health literacy encompasses not basic reading and numeracy skills as well as the ability to evaluate evidence, appraise sources and apply information appropriately to one’s own circumstances. More recently, the concept of electronic health (eHealth) literacy has extended this framework to digital contexts, highlighting competencies such as online navigation, source verification and critical appraisal of web-based content ( 24 ). Higher educational attainment and stronger numeracy skills are consistently associated with greater confidence in evaluating health information and lower susceptibility to some forms of misleading content ( 25 ). However, there is growing recognition that traditional literacy frameworks may be insufficient in the current information ecosystem. Contemporary misinformation frequently exploits emotional framing such as sensational headlines, social identity cues, visual manipulation and synthetic media rather than straightforward factual inaccuracies. Evidence is often cherry-picked or misapplied. AI-generated images, videos and text can convincingly mimic authoritative sources, while impersonation of official institutions or professionals blurs conventional credibility heuristics. These developments raise important questions about whether skills such as numeracy and evidence appraisal meaningfully protect against newer, technologically mediated forms of deception and manipulation ( 26 ). Moreover, the association between misinformation exposure and health behaviour remains under-theorised and under-measured ( 27 ). Most of the existing evidence focuses on belief accuracy, sharing intentions or attitudinal outcomes, rather than concrete behavioural consequences. Where behavioural outcomes are examined, studies often centre on adherence to specific public health interventions (such as vaccination uptake) or on acute crisis contexts as was observed during the COVID-19 pandemic ( 28 ). Less attention has been paid to everyday self-care decisions and routine care-seeking behaviours, despite their cumulative importance for population health and healthcare system functioning ( 11 , 29 ). Other than in the case of common self-limiting conditions, delayed care-seeking represents a particularly salient outcome in this regard as it may contribute to disease progression, missed opportunities for early intervention and increased treatment costs. While structural barriers such as access, cost and availability are well-established determinants of delayed care ( 30 ), informational factors (including uncertainty generated by misleading or conflicting health claims) may also play an important role ( 31 , 32 ). Qualitative studies suggest that individuals exposed to contradictory advice may postpone seeking care while attempting further self-management or information verification, especially when confidence in professional advice is undermined ( 33 , 34 ). Socioeconomic factors add another layer of complexity to this picture as the implications of exposure to misinformation are not evenly distributed across populations. Structural inequities, differential trust in institutions, language barriers and prior experiences of discrimination may shape both information environments and responses to misleading content ( 35 ). Urbanicity may also matter, since densely populated urban settings are associated with higher information saturation and platform use, potentially increasing exposure frequency. Yet empirical evidence disentangling these patterns in general adult populations remains limited ( 36 – 38 ). Within the UK context, there is a notable gap in large-scale, population-based studies examining how exposure to health misinformation intersects with self-care confidence, trust in information sources and care-seeking behaviour other than public health crisis. Much of the UK-based evidence emerged from platform-specific analyses, qualitative studies, or pandemic-era surveys, limiting generalisability to routine health decision-making. Moreover, few studies explicitly differentiate between types of misinformation tactics such as sensational framing versus AI-enabled deception or assess whether commonly promoted literacy skills are differentially effective against them ( 39 , 40 ). The present study addresses these gaps by examining exposure to health misinformation among UK adults, situated within the everyday practices of self-care and health information use. Rather than treating misinformation solely as a problem of false belief, this work conceptualises it as a potential disruptor of decision-making processes, with implications for confidence, trust and the timing of professional care-seeking. By integrating measures of exposure frequency, perceived recognition ability, behavioural responses and health literacy skills, the study provides a granular assessment of where and how informational harms may arise, even in a population that reports high confidence in self-care and strong trust in professional sources. The aim of this study was to generate population-level evidence on exposure to health misinformation and its behavioural implications within contemporary self-care. Specifically, we sought to (i) quantify the frequency and nature of health misinformation encountered by the UK adults, including the prevalence of specific misinformation tactics, (ii) examine sociodemographic and educational predictors of the self-reported ability to recognise misinformation, with particular attention to inequalities across ethnic and geographic groups, (iii) assess the consequences of exposure for safe self-care, specifically examining dose-response associations with delays in professional care-seeking and the erosion of decision-making confidence, and (iv) examine whether traditional health literacy skills (numeracy) remain protective against emerging misinformation types, comparing their association with argument-based versus AI-enabled and identity-based tactics. Methods Design and reporting standard We conducted a cross-sectional, self-administered online survey of adults living in the UK. Reporting complied with the Checklist for Reporting Results of Internet ESurveys (CHERRIES); Supplementary file 1 . Setting, dates and eligibility The online survey was open from 1 December 2025 to 5 January 2026. Eligibility required age ≥ 18 years and the ability to read English. Participants accessed an anonymous web link and completed the instrument on desktop or mobile devices. Sampling frame, openness and recruitment Participants were recruited exclusively via the Prolific Academic panel using quota sampling to approximate the UK adult population by age, gender and ethnicity. The survey was implemented as a closed panel study, with access restricted to Prolific participants via unique panel identifiers. No targeted incentives beyond standard panel compensation were used. Because recruitment was conducted through a closed panel, CHERRIES view and participation rates are defined by the panel provider rather than site visitor counts; completion among eligible respondents is reported below. This sampling approach prioritised internal validity and comparability across respondents over maximising reach. Platform and delivery The instrument was implemented in the Qualtrics XM Platform (Qualtrics International Inc., Provo, UT). The platform captured timestamps, device metadata, IP address and coarse geolocation if permitted by the browser. These variables were used solely for data quality checks and are not reported at an identifiable level. The instrument used concise items, varied stems and mandatory responses with neutral options where appropriate. Device type and completion time distributions were inspected to flag implausible submissions; no thresholds triggered exclusion. Instrument development and pretesting The survey comprised six main sections assessing: (i) sociodemographic and social characteristics, (ii) health status and self-care practices; (iii) sources of health information, frequency of use, and trust; (iv) exposure to misleading health content and recognition of misinformation tactics; (v) health literacy and numeracy; and (vi) behavioural responses to misinformation and conflicting claims. Numeracy was assessed using items on the ability to interpret simple risk statistics (e.g., "1 in 10") and read medicine labels correctly, both rated on five-point scales (strongly disagree to strongly agree). Misinformation recognition was measured across five common tactics: sensational headlines, cherry-picked evidence, fake experts or credentials, AI-generated images or videos, and impersonation of official sources, each rated on a five-point frequency scale (never to very often). Trust in information sources was measured on five-point scales (not at all to completely) across professional sources (doctors, nurses, pharmacists), institutional sources (NHS, government/Ministry of Health websites, WHO), and non-professional sources (social media platforms including Facebook, YouTube, TikTok; news media; influencers; and AI-based tools). Self-care practices were assessed by self-reported use of prescription medicines, over-the-counter medicines, supplements and herbal products in the preceding three months, and maintenance of a basic home care kit. Barriers to and benefits of self-care were measured as endorsements of predefined items (cost, time, information overload, distrust of sources, cultural beliefs and availability of services for barriers; autonomy, disease prevention, faster symptom relief, fewer clinic visits, and lower cost for benefits). The questionnaire underwent internal piloting to refine wording and routing logic before launch. A copy of the survey is included in Supplementary file 2 . Page design, adaptive questioning, completeness checks and timing The survey used multiple pages with a visible progress indicator. Limited branching logic was applied: detailed questions about the frequency of use of specific health information sources were displayed only to those who reported any use of those sources; similarly, detailed items about specific misinformation tactics were displayed only to those who reported exposure to misleading health content in the preceding six months. Key outcomes (e.g., perceived ability to recognise misinformation, delayed care-seeking, trust ratings, self-care practices) were set as required items, with "Prefer not to say" offered for sensitive demographics. The platform enforced completeness checks before advancing between pages. Consistent with CHERRIES, we implemented post-hoc checks for potential duplicate participation. IP addresses were inspected for repetition. Outcomes and measures Primary outcomes were participants' perceived ability to recognise misleading or inaccurate health information (rated on a five-point scale from "not at all true for me" to "completely true for me") and frequency of delayed professional care-seeking following exposure to misleading health content (rated as never, rarely, sometimes, often, or very often). Secondary outcomes included frequency of exposure to false or misleading health content in the preceding six months (never, once or twice, monthly, weekly, or daily), recognition of specific misinformation tactics (sensational headlines, cherry-picked evidence, fake experts, AI-generated images or videos, and impersonation of official sources), trust in various information sources (professional, institutional, social media, news media, influencers, and AI tools), self-care engagement and confidence, perceived barriers to and benefits of self-care, and health literacy. Additional behavioural outcomes included scenario-based responses to hypothetical misinformation exposures on topics such as vaccine safety, weight-loss medicines, herbal remedies, AI-generated content, experimental treatments, and celebrity health claims. All item texts and coding rules are provided in the Supplement and in the statistical analysis plan. Statistical analysis Descriptive statistics summarised participant sociodemographic characteristics, health indicators, self-care practices, information sources, trust in sources, and exposure to misleading health content using frequencies, percentages, mean and standard deviation. For ordinal or Likert-type items, full distributions were presented. Bivariate associations were explored using Spearman's rank correlation (non-parametric test) for ordinal variables. Associations between numeracy and recognition of specific misinformation tactics were assessed using Spearman's correlation coefficients, with Bonferroni correction applied to adjust for multiple comparisons. The association between perceived erosion of confidence from conflicting claims and overall confidence in self-care decision-making was also assessed using Spearman's correlation coefficient. Multivariable analyses employed ordinal logistic regression to estimate unadjusted and adjusted odds ratios (ORs and aORs) with 95% confidence intervals (CIs). The first set of models examined predictors of participants' perceived ability to recognise misleading health information, including educational attainment, age, sex, ethnicity, urbanicity, and participation in community or religious activities. The second set assessed predictors of delayed professional care-seeking, with a focus on frequency of exposure to false or misleading health content alongside demographic covariates. Fully adjusted models included all key sociodemographic variables, and robust standard errors were computed to account for potential heteroscedasticity. Statistical significance was set at p < 0.05, with Bonferroni correction applied where multiple comparisons were made. All analyses were performed using STATA, version 18 (StataCorp LP, College Station, TX, USA). Ethics, consent and data protection The study received a favourable opinion from Imperial College Research Ethics Committee (ICREC #8061247) and complied with UK GDPR. Participants viewed an information sheet and provided electronic informed consent before answering any items; consent pages described purpose, voluntary nature, data handling and contact details. No direct personal identifiers were collected. IP addresses and coarse location, captured by the platform, were accessible only to the core research team for fraud and duplication checks and were not used to reidentify participants. Data were stored on secure institutional servers and analysed in deidentified form. Patient and public involvement The survey was developed by the research team and after several iterations, the instrument was passed on to three lay members for comments. The survey was iterated again among the research team partners to arrive at the final version. Results Sample characteristics A total of 1,414 adults residing in the UK completed the survey. The main survey findings are included in Supplementary Table 1 . Participant characteristics are illustrated in Table 1 . The mean age of participants was 46.8 years (SD 15.8; range 18–88), with a near-equal distribution by sex (51.8% female, 47.7% male). Almost half of the respondents lived in urban areas (46.1%), with the remainder residing in peri-urban (33.7%) or rural settings (20.2%). The sample was predominantly White (84.9%), with smaller proportions identifying as Asian (7.4%), Black (3.5%), Mixed (2.2%), or Other ethnic backgrounds (1.1%). Socioeconomic indicators suggested a largely economically active population: 67.4% were in paid employment, 15.1% were retired and 4.5% were unemployed. Educational attainment was high, with 59.3% reporting university or postgraduate qualifications. Despite this, financial strain was evident; 17.9% reported sometimes or often skipping essentials due to cost and only 16.2% reported holding private health insurance. Social capital indicators were mixed. While most participants reported having at least one person they could rely on for support in a health crisis, 7.3% reported having no such support. More than half never participated in community or religious activities and loneliness was reported at least “some of the time” by 52.0% of respondents. Around 41% reported living with at least one long-term health condition, most commonly mental health or physical conditions. Table 1 Sociodemographic characteristics (N = 1,414) Variable Frequency (Percentage) What is your age? Mean( 41 ) 46.8 (15.8) Range 18.0–88.0 What sex were you assigned at birth? Female 732 (51.8%) Male 675 (47.7%) Prefer not to say 7 (0.5%) In which country do you currently live? UK 1414 (100%) In what type of area do you live in? Rural 286 (20.2%) Peri-urban 476 (33.7%) Urban 652 (46.1%) How would you describe your ethnic or cultural identity? White 1201 (84.9%) Asian 104 (7.4%) Black 50 (3.5%) Mixed 31 (2.2%) Other 15 (1.1%) Prefer not to say 13 (0.9%) What is your current main work status? Employed full-time 702 (49.6%) Employed part-time 251 (17.8%) Homemaker 54 (3.8%) Retired 214 (15.1%) Student 61 (4.3%) Unable to work 39 (2.8%) Unemployed 64 (4.5%) Other 29 (2.1%) If employed, what type of work do you do? Agriculture/fishing 3 (0.2%) Professional/technical 638 (45.1%) Service/sales 158 (11.2%) Skilled manual 88 (6.2%) Unskilled labour 35 (2.5%) Other 105 (7.4%) Not applicable 387 (27.4%) How religious or spiritual would you describe yourself? Not at all religious or spiritual 772 (54.6%) Slightly religious or spiritual 332 (23.5%) Moderately religious or spiritual 159 (11.2%) Very religious or spiritual 91 (6.4%) Extremely religious or spiritual 34 (2.4%) Prefer not to say 26 (1.8%) What is the highest level of education you have completed? No formal schooling 2 (0.1%) Primary 4 (0.3%) Secondary 283 (20.0%) Vocational/technical 277 (19.6%) University 533 (37.7%) Postgraduate 305 (21.6%) Prefer not to say 10 (0.7%) Do you sometimes skip essentials (food, utilities, healthcare) because of cost? Never 777 (55.0%) Rarely 384 (27.2%) Sometimes 205 (14.5%) Often 34 (2.4%) Very often 14 (1.0%) Do you have private health insurance? Yes 229 (16.2%) No 1185 (83.8%) How many people can you rely on for support in a health crisis? 1–2 701 (49.6%) 3–5 488 (34.5%) 6+ 122 (8.6%) None 103 (7.3%) How often do you participate in community or religious group activities? Never 783 (55.4%) Rarely 335 (23.7%) Sometimes 178 (12.6%) Often 89 (6.3%) Very often 29 (2.1%) How often do you feel lonely? Never 298 (21.1%) Hardly ever 380 (26.9%) Some of the time 361 (25.5%) Occasionally 273 (19.3%) Often / Always 102 (7.2%) Do you live with a long-term disability or health condition? Cognitive 9 (0.6%) Mental health 211 (14.9%) Sensory 19 (1.3%) Physical 241 (17.0%) Other 51 (3.6%) None 837 (59.2%) Prefer not to say 46 (3.3%) What is your migration status? Born in your current country of residence 1225 (86.6%) Immigrant ≤ 5 years 40 (2.8%) Immigrant > 5 years 117 (8.3%) Prefer not to say 32 (2.3%) How well do you read in the survey language? Not at all 1 (0.1%) Basic 1 (0.1%) Intermediate 8 (0.6%) Advanced 41 (2.9%) Native/Fluent 1363 (96.4%) Health status and self-care practices Self-rated health was generally favourable, with 64.3% reporting good or very good health, although 35.7% rated their health as fair or poor. Over half of respondents (54.7%) reported no chronic conditions ( Supplementary Table 1) . Among those with long-term conditions, mental health conditions (20.4%) and hypertension (11.9%) were most frequently reported. Engagement in self-care using pharmaceutical and other products was widespread. In the preceding three months, 55.9% had used prescription medicines, 60.0% had used over-the-counter medicines and 26.5% had used supplements or herbal products. The majority (90.2%) reported maintaining a basic home care kit. Confidence in self-care was high. Nearly all respondents agreed or strongly agreed that they could manage minor ailments at home (98.3%), knew when to seek professional care (92.9%) and knew where to find trustworthy self-care guidance (91.4%). Self-care was endorsed as a first step before consulting a healthcare professional by 92.0% of participants and 78.0% agreed or strongly agreed that they practised self-care regularly. Perceived benefits of self-care included fewer clinic visits (66.0%) as delay in these instances may be deemed appropriate, faster symptom relief (61.2%) and disease prevention (51.3%). However, barriers were also common, particularly cost (40.1%), time constraints (33.4%), information overload (30.2%) and distrust of information sources (31.2%). Just over one-quarter (26.1%) reported no barriers to practising self-care. Information sources, trust and exposure to misleading health content Professional and institutional sources dominated health information use and trust. The National Health Service was frequently consulted (60.9% often or very often) and highly trusted (76.4% very or complete trust), as were doctors (40.3% often/very often used; 73.7% very or completely trusted), nurses (26.8%; 64.6%), and pharmacists (27.5%; 60.9%); Supplementary Table 1 . Universities and research institutes were less frequently consulted (12.5% often/very often) but remained moderately trusted (51.3% very or completely). In contrast, social media platforms and influencers were rarely used and widely distrusted. Most respondents reported never using Facebook (78.4%), TikTok (80.8%), or influencers (80.3%) for health information, and over 80% reported no trust at all in these sources. News media were also poorly trusted, with only 4.2% reporting very or complete trust. Use of AI-based tools and chatbots was limited (12.1% often or very often used), and trust was low: 37.0% reported no trust and only 9.4% reported very or complete trust. Exposure to health misinformation was common. In the preceding six months, 81.8% of respondents reported encountering false or misleading health content at least once and 65.3% reported exposure to content they believed was deliberately false. The most frequently cited topics included nutrition and weight loss, supplements, vaccines and weight-loss medicines. Participants most frequently reported encountering sensational headlines (57.7% often or very often) and cherry-picked evidence (52.2%), followed by fake experts or credentials (33.8%). AI-generated images or videos (40.8%) and impersonation of official sources (17.5%) were also reported as occurring often or very often. Despite this high exposure, most respondents rated their ability to recognise misleading information as “mostly true” or “completely true” (51.8%), although confidence declined when participants reported encountering conflicting health claims. Educational attainment and perceived ability to recognise misinformation Ordinal logistic regression examined sociodemographic predictors of self-reported ability to recognise misleading health information (Table 2 ). Higher educational attainment showed a strong, graded association with perceived recognition ability. Compared with secondary education, university education (adjusted odds ratio [aOR] 2.05, 95% CI 1.57–2.68) and postgraduate education (aOR 2.29, 95% CI 1.69–3.10) were associated with significantly higher odds of reporting greater recognition ability. Vocational or technical education showed no significant association. Urbanicity was independently associated with perceived recognition ability. Participants living in rural (aOR 1.59, 95% CI 1.20–2.11) or peri-urban areas (aOR 1.44, 95% CI 1.15–1.80) reported higher recognition ability compared with urban residents; Table 2 . Asian participants had substantially lower odds of reporting high recognition ability than White participants (aOR 0.39, 95% CI 0.26–0.60), while no significant differences were observed for Black, Mixed, or Other ethnic groups. Increasing age was associated with lower perceived ability to recognise misinformation. Sex and community or religious participation were not significantly associated after adjustment. Table 2 Unadjusted and adjusted ordinal logistic regression models examining educational attainment, demographics and social predictors of self-reported ability to recognise health misinformation Variable Unadjusted Adjusted* OR 95% CI p-value aOR 95% CI p-value Highest level of education completed No formal schooling 1.78 0.44–7.23 0.422 1.67 0.48–5.81 0.418 Primary 2.82 0.38–21.11 0.313 2.49 0.32–19.59 0.386 Secondary Ref Ref Vocational/technical 1.22 0.89–1.67 0.223 1.13 0.82–1.57 0.452 University 2.02 1.55–2.63 < 0.001 2.05 1.57–2.68 < 0.001 Postgraduate 2.18 1.62–2.94 < 0.001 2.29 1.69–3.10 < 0.001 Sex Female Ref Ref Male 1.12 0.93–1.36 0.240 1.17 0.95–1.43 0.133 Age 0.99 0.98–0.99 < 0.001 0.98 0.98–0.99 < 0.001 Ethnicity White Ref Ref Asian 0.47 0.32–0.70 < 0.001 0.39 0.26–0.60 < 0.001 Black 0.74 0.47–1.16 0.193 0.67 0.39–1.14 0.141 Mixed 0.88 0.43–1.81 0.728 0.71 0.32–1.54 0.380 Other 1.03 0.27–3.86 0.964 0.86 0.24–3.08 0.811 Urbanicity Rural 1.47 1.13–1.91 0.004 1.59 1.20–2.11 0.001 Peri-urban 1.45 1.17–1.80 0.001 1.44 1.15–1.80 0.001 Urban Ref Ref Community/religious activities participation Never Ref Ref Rarely 0.95 0.76–1.20 0.679 0.91 0.71–1.17 0.461 Sometimes 0.84 0.63–1.13 0.259 0.92 0.68–1.26 0.611 Often 0.97 0.68–1.39 0.882 0.97 0.66–1.41 0.862 Very often 0.89 0.50–1.59 0.695 0.81 0.47–1.39 0.443 *Fully adjusted model includes age, sex, ethnicity, education, urbanicity and community/religious participation. Robust standard errors used. Exposure to misleading information and delays in professional care-seeking Just over half of respondents (51.6%) reported never delaying professional care due to online health claims, while 30.8% reported rare delays and 17.6% reported sometimes or more frequent delays. In adjusted ordinal logistic regression, exposure to misleading health content showed a clear dose-response relationship with delays in care-seeking. Unadjusted and adjusted ordinal logistic regression models examining exposure to false/misleading health content, educational attainment and demographics as predictors of delays in professional care seeking are shown in Table 3 . Compared with respondents reporting no exposure, those exposed once or twice had higher odds of delay (aOR 1.53, 95% CI 1.11–2.12). Monthly exposure was associated with further increased odds (aOR 1.83, 95% CI 1.27–2.63) and weekly exposure showed the highest risk (aOR 1.89, 95% CI 1.31–2.71). Daily exposure was not significantly associated with additional risk. Older age was associated with lower odds of delay. Asian (aOR 2.35, 95% CI 1.63–3.40) and Black participants (aOR 1.86, 95% CI 1.18–2.91) had significantly higher odds of delaying professional care compared with White participants. Peri-urban residence was associated with lower odds of delay compared with urban residence. Educational attainment and sex were not independently associated with delays after adjustment. Table 3 Unadjusted and adjusted ordinal logistic regression models examining exposure to false/misleading health content, educational attainment and demographics as predictors of delays in professional care seeking Variable Unadjusted Adjusted* OR 95% CI p-value aOR 95% CI p-value False/misleading health content exposure (past 6 months) Never Ref. Ref. Once/twice 1.73 1.28–2.33 < 0.001 1.53 1.11–2.12 0.010 Monthly 2.37 1.70–3.32 < 0.001 1.83 1.27–2.63 0.001 Weekly 2.20 1.58–3.08 < 0.001 1.89 1.31–2.71 0.001 Daily 1.45 0.81–2.59 0.206 1.27 0.70–2.28 0.430 Highest level of education completed Secondary Ref. - Ref. - No formal schooling 5.36 1.74–16.53 0.003 4.42 0.42–46.81 0.217 Primary 0.90 0.23–3.61 0.886 0.71 0.10–5.11 0.736 Vocational/technical 1.12 0.81–1.56 0.498 1.10 0.78–1.54 0.599 University 1.22 0.93–1.60 0.159 0.97 0.73–1.30 0.855 Postgraduate 1.11 0.82–1.50 0.512 0.84 0.60–1.17 0.307 Sex Female Ref. Ref. Male 0.89 0.73–1.09 0.254 0.90 0.73–1.10 0.305 Age 0.97 0.97–0.98 < 0.001 0.98 0.97–0.99 < 0.001 Ethnicity White Ref. Ref. Asian 2.94 2.13–4.08 < 0.001 2.35 1.63–3.40 < 0.001 Black 2.19 1.43–3.35 < 0.001 1.86 1.18–2.91 0.007 Mixed 1.81 0.83–3.93 0.135 1.38 0.63-3.00 0.419 Other 3.00 1.15–7.85 0.025 2.54 1.00-6.47 0.051 Urbanicity Urban Ref - Ref. - Rural 0.57 0.43–0.76 < 0.001 0.76 0.57–1.02 0.064 Peri-urban 0.64 0.51–0.80 < 0.001 0.71 0.56–0.90 0.004 Numeracy and recognition of misinformation tactics Spearman’s rank correlation analyses assessed associations between numeracy skills and recognition of common misinformation tactics, with Bonferroni correction applied (Table 4 , Fig. 1 ). Numeracy showed robust positive associations with recognition of sensational headlines (ρ = 0.156, p < 0.001) and cherry-picked evidence (ρ = 0.158, p < 0.001), both surviving correction for multiple comparisons. No meaningful associations were observed between numeracy and recognition of AI-generated images or videos, or impersonation of official sources. Recognition of fake experts or credentials showed a weak association that did not survive correction. These findings indicate that numeracy skills were associated with detection of argument-based misinformation but not with technologically mediated or identity-based deception. Table 4 Spearman's rank correlation between numeracy skills and recognition of misinformation tactics: analysis with Bonferroni multiple comparisons correction (α = 0.01) Misinformation Tactic Spearman's rho p-value Survives Bonferroni Correction Interpretation Sensational headlines 0.1557 < 0.001 Yes (p < 0.01) Robust positive association: Higher numeracy significantly predicts better recognition of sensational framing Cherry-picked evidence 0.1583 < 0.001 Yes (p 0.01) Spurious positive: Appears significant at the conventional level but is a false positive after correction for multiple testing AI-generated images/videos 0.0077 0.774 No No association: Numeracy skill unrelated to recognising AI-generated content; statistical literacy does not transfer to technological deception detection Impersonation of official sources -0.0441 0.097 No No association: Weak negative trend, not statistically significant; numeracy does not predict detection of identity-based manipulation Behavioural responses to health misinformation Behavioural responses following exposure to health misinformation were predominantly cautious. Most respondents reported verifying information before acting, relying on official sources, or seeking professional advice; Supplementary Table 1 . However, potentially risky behaviours were not uncommon. Nearly half of respondents reported substituting self-care for professional consultation at least sometimes following exposure, whereas 13.9% reported sometimes or more frequent cessation of medicines without further advice. Scenario-based items consistently showed low endorsement of believing or sharing unverified claims, with most respondents indicating that they would verify information, consult trusted sources, or ignore misleading content. Conflicting claims and confidence in self-care decision-making Almost half of the respondents agreed that encountering conflicting health claims reduced their confidence in self-care decisions. Spearman’s correlation analysis demonstrated a significant negative association between agreement that conflicting claims reduce confidence and overall confidence in self-care decision-making (ρ= −0.142, p < 0.001), indicating that perceived informational conflict was associated with lower self-care confidence (Fig. 2 ). Discussion Summary of principal findings This study provides a comprehensive population-level examination of self-care practices, health information use, trust and exposure to pharmaceutical misinformation within a contemporary digital ecosystem in the UK. Several findings are particularly salient. First, engagement in self-care was widespread and confident as most respondents reported high capability in managing minor ailments, strong knowledge of when to seek professional care and routine use of prescription, over-the-counter and supplementary products. Trust in professional and institutional sources, especially the NHS, doctors, nurses and pharmacists, remained high, despite frequent exposure to misleading health content. Second, exposure to false or misleading pharmaceutical and health information was pervasive. Over four-fifths of respondents reported encountering misleading content in the preceding six months, commonly related to nutrition, supplements, vaccines and weight-loss medicines. Recognition of common misinformation tactics such as sensational headlines and cherry-picked evidence was widespread, yet confidence declined when individuals were confronted with conflicting claims. Third, educational attainment emerged as a strong and independent predictor of perceived ability to recognise misinformation, with a clear gradient favouring university and postgraduate education. However, this apparent “literacy advantage” did not uniformly translate into behavioural protection. Exposure to misleading content was associated with a graded increase in delays in professional care-seeking, even after adjustment for education, age, sex, ethnicity and urbanicity. Notably, higher exposure frequencies showed diminishing marginal effects, suggesting a possible saturation phenomenon. Fourth, inequities were evident as Asian and Black participants had significantly higher odds of delaying professional care following exposure to misleading information, while urban residents reported both lower perceived recognition ability and higher likelihood of delay compared with peri-urban and rural respondents. These patterns highlight that susceptibility to misinformation is not solely a function of individual cognitive skill but is socially and contextually patterned. Fifth, health literacy skills showed differentiated effects. Numeracy was associated with improved recognition of argument-based misinformation tactics (sensational framing and selective evidence), but not with detection of technologically mediated deception such as AI-generated imagery or impersonation of official sources. This dissociation highlights a critical gap between traditional health literacy competencies and emerging forms of digital manipulation. However, delayed professional care-seeking should not be interpreted uniformly as inappropriate or unsafe behaviour. In the context of self-care for common, self-limiting conditions, a period of watchful waiting is explicitly endorsed within UK guidance. NHS and NICE advice encourages individuals to manage minor illness at home and to seek professional input only if symptoms persist beyond their expected natural course or worsen. For example, NHS guidance advises that an acute cough may reasonably last up to three weeks before medical review is required, while sore throat symptoms typically resolve within around one week ( 42 , 43 ). Clear communication about normal symptom duration has been a cornerstone of national strategies to reduce unnecessary consultations for minor illness. In this regard, these findings therefore challenge the implicit assumption that all care-seeking delay reflects misinformation-related harm or poor decision-making. Instead, they highlight the need to distinguish between appropriate, guideline-concordant delay in self-limiting conditions and potentially harmful delay in situations where professional assessment is clinically indicated. Finally, the analysis distinguishes between argument-based misinformation tactics (such as sensational headlines and cherry-picked evidence) and technologically mediated or identity-based tactics (including AI-generated content and impersonation of official sources). This distinction reflects emerging concerns that the evolving nature of health misinformation may outpace traditional educational and literacy-based countermeasures. In doing so, the study contributes empirically to ongoing debates about whether current public health strategies, largely focused on individual capability-building, are sufficient to safeguard timely access to care in a rapidly changing information environment. Together, this work positions misleading health information as not only a challenge to knowledge accuracy but also a potential patient safety and access issue within routine healthcare systems. Understanding its behavioural implications is essential for designing proportionate, equity-sensitive responses that support effective self-care while preserving trust in professional care pathways. Comparison with existing literature The high prevalence of exposure to misleading health information observed in this study aligns with international evidence describing the normalisation of misinformation within everyday digital consumption. Prior work has consistently shown that false or misleading health content circulates widely and is often encountered incidentally rather than sought deliberately ( 15 , 17 , 25 , 44 ). The present findings extend this literature by demonstrating that such exposure is now near-ubiquitous even in a highly educated UK sample with strong trust in professional health institutions. The association between misinformation exposure and delayed care-seeking corroborates earlier experimental and observational studies showing that misinformation can influence not only beliefs but downstream health behaviours ( 16 – 20 ). Importantly, this study moves beyond vaccination-specific contexts to demonstrate similar behavioural risks across a broader pharmaceutical and self-care landscape, including medicines use, supplements and chronic disease claims. The observed dose–response relationship reinforces concerns that cumulative exposure may incrementally erode timely engagement with formal healthcare services. Educational gradients in misinformation recognition mirror well-established health literacy research, which consistently links higher educational attainment to greater confidence and competence in navigating health information. However, the absence of a protective effect of education against care-seeking delays is notable ( 1 , 5 , 45 ). These findings challenge implicit assumptions that improving factual knowledge or critical appraisal skills alone will suffice to mitigate behavioural harms. Instead, it supports emerging evidence that even highly literate individuals may experience decisional paralysis, mistrust, or delay when confronted with conflicting or emotionally salient information ( 20 , 46 , 47 ). The urban disadvantage observed in both perceived recognition ability and care-seeking behaviour contrasts with common narratives that associate urban residence with superior access to information and services ( 38 ). This pattern may reflect higher exposure intensity, greater information overload, or more fragmented trust environments in densely networked urban settings. While speculative, this interpretation is consistent with sociological accounts of “information saturation” and warrants closer investigation ( 48 ). The differential associations by ethnicity are consistent with broader literature documenting structural and historical drivers of mistrust, differential exposure pathways and unequal health system experiences among minority ethnic groups ( 49 – 51 ). Importantly, these findings should not be interpreted as evidence of intrinsic vulnerability, but rather as signals of uneven informational and institutional environments that shape how misinformation is encountered and acted upon. Finally, the selective association between numeracy and recognition of specific misinformation tactics aligns with cognitive psychology research distinguishing analytic reasoning from perceptual or identity-based deception detection ( 25 ). Traditional health literacy frameworks have emphasised statistical understanding and evidence appraisal; however, the present findings suggest these skills offer limited protection against synthetic media and impersonation tactics increasingly enabled by generative AI ( 26 , 37 ). Implications for research and practice First, these findings highlight that trust in professional and institutional sources remains a critical protective asset. Efforts to counter pharmaceutical misinformation should therefore prioritise strengthening the visibility, accessibility and communicative clarity of trusted sources rather than relying solely on debunking false claims. Second, the observed behavioural impact of misinformation exposure, particularly delays in care, highlights the need to integrate misinformation risk explicitly into patient safety and access frameworks. Delayed presentation can have material consequences for disease progression, treatment effectiveness and health system costs. Monitoring misinformation-related delays should therefore be considered alongside more established quality and safety indicators. Third, the findings suggest that conventional health literacy interventions may be necessary but insufficient. While numeracy and critical appraisal skills support the detection of certain misinformation forms, they do not equip individuals to identify AI-generated content or impersonation of authority. This points to the need for updated literacy models that incorporate digital provenance cues, platform dynamics and heuristic recognition of manipulation techniques. Fourth, the persistence of inequities by ethnicity and urbanicity indicates that universal messaging strategies are unlikely to be uniformly effective. Tailored, community-embedded approaches that acknowledge differential trust histories and exposure contexts are likely to be required. Pharmacists especially may represent an under-utilised intermediary given their high trust levels and accessibility. Finally, the low trust in social media platforms, influencers and news media coupled with strong support for transparency, external verification and quality assurance, suggests public appetite for more visible governance mechanisms. While this study does not evaluate regulatory interventions directly, it supports the principle that trust-building features should be additive and explicit rather than assumed. Strengths and limitations This study has several strengths. It draws on a large UK sample with broad demographic coverage and provides granular data across self-care behaviours, information sources, trust, exposure and behavioural responses. The inclusion of inferential analyses allows identification of independent predictors while controlling for key sociodemographic factors. The use of scenario-based items enhances ecological validity by approximating real-world decision contexts. However, limitations should be acknowledged. The cross-sectional design precludes causal inference and associations between exposure and behaviour may be bidirectional. Self-reported measures of exposure, recognition ability and behaviour are subject to recall and social desirability biases. Educational attainment was high and the sample was predominantly White, which may limit generalisability to more socioeconomically disadvantaged or ethnically diverse populations. Perceived ability to recognise misinformation may not correspond to objective accuracy and future work should incorporate performance-based measures. Additionally, while the study captures a wide range of misinformation types, it does not disentangle specific platform effects or content features in detail. Nor does it directly assess clinical outcomes associated with delayed care. We also acknowledge the absence of data on respondents’ affiliation with the health sector. Collecting such data would be helpful to understand more deeply the factors that influence how health misinformation is interpreted and evaluated. These gaps represent important avenues for future investigation. Future research Future research should prioritise longitudinal designs to clarify temporal relationships between exposure, trust erosion and health behaviours. Linking survey data to healthcare utilisation records could help quantify the clinical and economic consequences of misinformation-related delays. Experimental studies are needed to test which trust-enhancing features, such as quality assurance labels, third-party verification, or transparent sourcing, most effectively mitigate behavioural harms. Further work should also examine how generative AI reshapes misinformation exposure and detection, particularly as synthetic media becomes more sophisticated. Developing and validating new literacy instruments that capture digital provenance skills will be essential. Finally, participatory research with minoritised and urban communities is needed to co-design interventions that address structural drivers of vulnerability rather than placing responsibility solely on individuals. Conclusion This UK study found widespread and confident self-care, particularly relating to the use of pharmaceuticals and other products, rooted in strong trust in professional sources, yet nearly universal exposure to pharmaceutical misinformation with clear behavioural impacts, including delayed care-seeking. Higher education improved perceived recognition but did not remove behavioural risk, and inequities persisted by ethnicity and urbanicity. Traditional literacy skills aided detection of some misinformation but were insufficient against AI-enabled tactics. These findings show that pharmaceutical misinformation represents a systemic challenge requiring equity-focused, system-level responses alongside individual literacy support. Declarations Conflicts of interest The authors report no conflicts of interest. Funding This research did not receive any funding. Acknowledgments AEO and AM are supported by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration Northwest London. The views expressed in this publication are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. Author Contributions All authors provided substantial contributions to the conception (AEO, PS), design (AEO, SA, AA), acquisition (SA, AEO) and interpretation (AEO, SA, DG, SAN, PS, DS, AM) of the study data. AEO took the lead in planning the study with support from co-authors. 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J R Soc Med 115(11):439–447 Hayanga B, Stafford M, Bécares L (2024) Ethnic inequalities in primary care experiences for people with multiple long-term conditions: Evidence from the general practice patient survey. Public Health 237:291–298 Irizar P, Kapadia D, Amele S, Bécares L, Divall P, Katikireddi SV et al (2023) Pathways to ethnic inequalities in COVID-19 health outcomes in the United Kingdom: A systematic map. Soc Sci Med 329:116044 Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryFile1MainSurvey.docx Supllementary File 1 SupplementaryFile2CHERRIESChecklist.docx Supplementary File 2 SupplementaryTable1.docx Supplementary Table 2 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8824194","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587825362,"identity":"071b029e-1c87-4d4b-ba3b-073857e1b21b","order_by":0,"name":"Austen El-Osta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie2RwUoDMRCGJwS2l0iuU5TuK2QRBFHwVeLJa8HLHmSJFHPqA9THEF9gyoB72QeoLIgieO6xIIqJFi+S1mMP+S6TDHzM/AlAJrObCPoug2sXi4LBT9tscqKCoOZrRf5bQbu+blO0k0RQN42+fb25H9dPB3oCYrkCPkwpSIUl6BixP/f9rLtUyCCHU+Cj9FbK8IcnhKjseauAAfYB+DRllKSXJHyD5eM8KJ9WlWHK+ybFkIKgSDQLERRnw1Ao4pTkYhUXJmYZ3nUxy4NVFQt/PDUXyfijdvLyHF5Mj9r2rR9f2bPQ4cWqPqlcypF/z8Jt+ciEnslkMplfvgAtBVO5eaETNQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8772-4938","institution":"Imperial College London","correspondingAuthor":true,"prefix":"","firstName":"Austen","middleName":"","lastName":"El-Osta","suffix":""},{"id":587825429,"identity":"c0dea41b-1fee-474c-b1f0-20e25d2759be","order_by":1,"name":"Sami Altalib","email":"","orcid":"https://orcid.org/0000-0001-7404-8486","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Sami","middleName":"","lastName":"Altalib","suffix":""},{"id":587825613,"identity":"ba16e619-218e-4a3f-9605-ad3654e8c135","order_by":2,"name":"Dumile Gumede","email":"","orcid":"https://orcid.org/0000-0002-8628-5726","institution":"Durban Univeristy of Technology","correspondingAuthor":false,"prefix":"","firstName":"Dumile","middleName":"","lastName":"Gumede","suffix":""},{"id":587825614,"identity":"3c994314-01f9-4632-992a-7ae2aa4d9780","order_by":3,"name":"Saja Alnahar","email":"","orcid":"https://orcid.org/0000-0003-1950-3073","institution":"Univeristy of Jordan","correspondingAuthor":false,"prefix":"","firstName":"Saja","middleName":"","lastName":"Alnahar","suffix":""},{"id":587825615,"identity":"4a5e102c-1714-4761-851f-7be5e57cbdd8","order_by":4,"name":"Savid Skinner","email":"","orcid":"","institution":"International Self-Care Foundation","correspondingAuthor":false,"prefix":"","firstName":"Savid","middleName":"","lastName":"Skinner","suffix":""},{"id":587825723,"identity":"65e1ecc7-edc6-44d4-a161-f1103f6afe0c","order_by":5,"name":"Peter Smith","email":"","orcid":"https://orcid.org/0000-0001-5418-7817","institution":"Self-Care Forum","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Smith","suffix":""},{"id":587825724,"identity":"034c734b-efab-421c-9c5c-e8e7bc9b19d1","order_by":6,"name":"Azeem Majeed","email":"","orcid":"https://orcid.org/0000-0002-2357-9858","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Azeem","middleName":"","lastName":"Majeed","suffix":""}],"badges":[],"createdAt":"2026-02-08 21:21:14","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8824194/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8824194/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102311448,"identity":"f7f17374-f57a-4251-a41c-5ef1952e2730","added_by":"auto","created_at":"2026-02-10 11:58:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182956,"visible":true,"origin":"","legend":"\u003cp\u003eDot plot (lollipop plot) of Spearman's rank correlations (ρ) between numeracy skills and recognition accuracy of five common health misinformation tactics, with colour-coding by statistical significance after Bonferroni correction for multiple comparisons (α=0.01)\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8824194/v1/e4da644aac4186f97644ab95.jpg"},{"id":102311271,"identity":"28918b36-e0f0-41cc-a75d-51225f48a502","added_by":"auto","created_at":"2026-02-10 11:57:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":285753,"visible":true,"origin":"","legend":"\u003cp\u003eA jittered scatterplot of the monotonic negative relationship between perceived erosion of confidence from conflicting health claims (x-axis: Strongly disagree to Strongly agree) and self-care decision confidence (y-axis: Strongly disagree to Strongly agree). The red LOWESS (Locally Weighted Scatterplot Smoothing) trend line illustrates the smoothed non-linear pattern\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8824194/v1/a42d39db23fd522debf4ef44.jpg"},{"id":102312161,"identity":"698fdfc0-3c32-4256-acd1-475c1fdb714d","added_by":"auto","created_at":"2026-02-10 12:00:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2403303,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8824194/v1/00e2cb14-0481-46e7-8b78-b471ba7dde93.pdf"},{"id":102310816,"identity":"113725c0-c7c6-4881-ad9f-392691563dd2","added_by":"auto","created_at":"2026-02-10 11:56:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":44100,"visible":true,"origin":"","legend":"\u003cp\u003eSupllementary File 1\u003c/p\u003e","description":"","filename":"SupplementaryFile1MainSurvey.docx","url":"https://assets-eu.researchsquare.com/files/rs-8824194/v1/7fb8617c569824ab268e7709.docx"},{"id":102311180,"identity":"3783e5d8-d033-4d02-b742-e1a36b9e3a78","added_by":"auto","created_at":"2026-02-10 11:57:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19881,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary File 2\u003c/p\u003e","description":"","filename":"SupplementaryFile2CHERRIESChecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-8824194/v1/11a7dcb2f53c1cb24152ddd9.docx"},{"id":102311300,"identity":"0f8579e9-1aa6-449a-866a-4a19e401aaad","added_by":"auto","created_at":"2026-02-10 11:57:42","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":58813,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 2\u003c/p\u003e","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8824194/v1/e19b687e7b9942f7bbb1f38e.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eExposure to health misinformation, self-care confidence and delayed care-seeking among adults in the UK: a cross-sectional survey\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Summary box","content":"\u003cp\u003e\u003cb\u003eWhat is already known on this topic\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eHealth and pharmaceutical misinformation is widely encountered online and could influence health beliefs, intentions and self-care practices.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHigher health literacy and educational attainment are associated with better recognition of misleading health information.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExisting evidence focuses on vaccines or pandemic contexts, with less attention to medicines, self-care and routine health decision-making among the general population.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhat this study adds\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eIn a representative UK sample, exposure to false or misleading pharmaceutical and health information was near-universal, despite high trust in professional health sources and strong self-care confidence.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExposure to misleading health content was associated with delayed professional care-seeking in a clear dose-response pattern, even after adjustment for education and sociodemographic factors.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHigher educational attainment increased perceived self-efficacy to recognise misinformation but did not protect against behavioural consequences such as care delays.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNumeracy skills were associated with recognition of sensational framing and cherry-picked evidence, but not with detection of AI-generated or impersonated content.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSocioeconomic-based variations were observed, with Asian and Black participants and urban residents exhibiting a higher risk of delayed care following exposure.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHow this study might affect research, practice or policy\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eHealth and medicines misinformation should be considered a patient safety and access issue, not solely a communication and promotion challenge.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInterventions focused only on individual health literacy are unlikely to be sufficient; trust-building, transparency and professional mediation remain critical.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHealth systems and regulators should account for emerging AI-enabled misinformation tactics that are poorly addressed by traditional health literacy approaches.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTargeted, culturally sensitive and patient centred strategies are needed to reduce misinformation-related harms in marginalised populations.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe ability to access, interpret and act on health information is fundamental to effective self-care and to the timely use of healthcare services. Across high-income health systems, self-care is increasingly framed as both an individual capability and a system-level necessity, enabling people to manage minor ailments, monitor long-term conditions and make informed decisions about when professional care is needed (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In the United Kingdom (UK), national policy and National Health Service (NHS) guidance actively promote self-management, supported by clear pathways for escalation to professional services. Yet the information environment in which these decisions are made has become increasingly complex, fragmented and contested (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the past decade, the volume and velocity of online health information has expanded dramatically. Social media platforms, messaging services, search engines and generative artificial intelligence (AI) tools now coexist alongside traditional professional and institutional sources (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). While this diversification has improved access to health knowledge, it has also increased exposure to misleading, inaccurate or deliberately deceptive health information - often referred to as health misinformation or disinformation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The World Health Organisation (WHO) has characterised this phenomenon as a global \u0026ldquo;infodemic\u0026rdquo;, warning that false or misleading health claims can undermine public trust, distort risk perceptions and generate harmful behaviours at scale (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAvailable literature has documented widespread exposure to health misinformation across diverse topics, including nutrition, vaccines, chronic disease \u0026ldquo;cures\u0026rdquo;, mental health and weight management (\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Importantly, misinformation does not operate solely by persuading individuals to adopt false beliefs but can also create uncertainty, confusion and decisional fatigue and paralysis. Experimental and observational studies suggest that exposure to conflicting or low-quality information might reduce confidence in health decisions, erode trust in healthcare institutions and delay engagement with healthcare services even among individuals who do not explicitly endorse false claims (\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHealth literacy has frequently been proposed as a protective factor against misinformation (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Conceptually, health literacy encompasses not basic reading and numeracy skills as well as the ability to evaluate evidence, appraise sources and apply information appropriately to one\u0026rsquo;s own circumstances. More recently, the concept of electronic health (eHealth) literacy has extended this framework to digital contexts, highlighting competencies such as online navigation, source verification and critical appraisal of web-based content (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Higher educational attainment and stronger numeracy skills are consistently associated with greater confidence in evaluating health information and lower susceptibility to some forms of misleading content (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, there is growing recognition that traditional literacy frameworks may be insufficient in the current information ecosystem. Contemporary misinformation frequently exploits emotional framing such as sensational headlines, social identity cues, visual manipulation and synthetic media rather than straightforward factual inaccuracies. Evidence is often cherry-picked or misapplied. AI-generated images, videos and text can convincingly mimic authoritative sources, while impersonation of official institutions or professionals blurs conventional credibility heuristics. These developments raise important questions about whether skills such as numeracy and evidence appraisal meaningfully protect against newer, technologically mediated forms of deception and manipulation (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, the association between misinformation exposure and health behaviour remains under-theorised and under-measured (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Most of the existing evidence focuses on belief accuracy, sharing intentions or attitudinal outcomes, rather than concrete behavioural consequences. Where behavioural outcomes are examined, studies often centre on adherence to specific public health interventions (such as vaccination uptake) or on acute crisis contexts as was observed during the COVID-19 pandemic (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Less attention has been paid to everyday self-care decisions and routine care-seeking behaviours, despite their cumulative importance for population health and healthcare system functioning (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOther than in the case of common self-limiting conditions, delayed care-seeking represents a particularly salient outcome in this regard as it may contribute to disease progression, missed opportunities for early intervention and increased treatment costs. While structural barriers such as access, cost and availability are well-established determinants of delayed care (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), informational factors (including uncertainty generated by misleading or conflicting health claims) may also play an important role (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Qualitative studies suggest that individuals exposed to contradictory advice may postpone seeking care while attempting further self-management or information verification, especially when confidence in professional advice is undermined (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Socioeconomic factors add another layer of complexity to this picture as the implications of exposure to misinformation are not evenly distributed across populations. Structural inequities, differential trust in institutions, language barriers and prior experiences of discrimination may shape both information environments and responses to misleading content (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Urbanicity may also matter, since densely populated urban settings are associated with higher information saturation and platform use, potentially increasing exposure frequency. Yet empirical evidence disentangling these patterns in general adult populations remains limited (\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin the UK context, there is a notable gap in large-scale, population-based studies examining how exposure to health misinformation intersects with self-care confidence, trust in information sources and care-seeking behaviour other than public health crisis. Much of the UK-based evidence emerged from platform-specific analyses, qualitative studies, or pandemic-era surveys, limiting generalisability to routine health decision-making. Moreover, few studies explicitly differentiate between types of misinformation tactics such as sensational framing versus AI-enabled deception or assess whether commonly promoted literacy skills are differentially effective against them (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). The present study addresses these gaps by examining exposure to health misinformation among UK adults, situated within the everyday practices of self-care and health information use. Rather than treating misinformation solely as a problem of false belief, this work conceptualises it as a potential disruptor of decision-making processes, with implications for confidence, trust and the timing of professional care-seeking. By integrating measures of exposure frequency, perceived recognition ability, behavioural responses and health literacy skills, the study provides a granular assessment of where and how informational harms may arise, even in a population that reports high confidence in self-care and strong trust in professional sources.\u003c/p\u003e \u003cp\u003eThe aim of this study was to generate population-level evidence on exposure to health misinformation and its behavioural implications within contemporary self-care. Specifically, we sought to (i) quantify the frequency and nature of health misinformation encountered by the UK adults, including the prevalence of specific misinformation tactics, (ii) examine sociodemographic and educational predictors of the self-reported ability to recognise misinformation, with particular attention to inequalities across ethnic and geographic groups, (iii) assess the consequences of exposure for safe self-care, specifically examining dose-response associations with delays in professional care-seeking and the erosion of decision-making confidence, and (iv) examine whether traditional health literacy skills (numeracy) remain protective against emerging misinformation types, comparing their association with argument-based versus AI-enabled and identity-based tactics.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign and reporting standard\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional, self-administered online survey of adults living in the UK. Reporting complied with the Checklist for Reporting Results of Internet ESurveys (CHERRIES); \u003cb\u003eSupplementary file 1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSetting, dates and eligibility\u003c/h3\u003e\n\u003cp\u003eThe online survey was open from 1 December 2025 to 5 January 2026. Eligibility required age\u0026thinsp;\u0026ge;\u0026thinsp;18 years and the ability to read English. Participants accessed an anonymous web link and completed the instrument on desktop or mobile devices.\u003c/p\u003e\n\u003ch3\u003eSampling frame, openness and recruitment\u003c/h3\u003e\n\u003cp\u003eParticipants were recruited exclusively via the Prolific Academic panel using quota sampling to approximate the UK adult population by age, gender and ethnicity. The survey was implemented as a closed panel study, with access restricted to Prolific participants via unique panel identifiers. No targeted incentives beyond standard panel compensation were used. Because recruitment was conducted through a closed panel, CHERRIES view and participation rates are defined by the panel provider rather than site visitor counts; completion among eligible respondents is reported below. This sampling approach prioritised internal validity and comparability across respondents over maximising reach.\u003c/p\u003e\n\u003ch3\u003ePlatform and delivery\u003c/h3\u003e\n\u003cp\u003eThe instrument was implemented in the Qualtrics XM Platform (Qualtrics International Inc., Provo, UT). The platform captured timestamps, device metadata, IP address and coarse geolocation if permitted by the browser. These variables were used solely for data quality checks and are not reported at an identifiable level. The instrument used concise items, varied stems and mandatory responses with neutral options where appropriate. Device type and completion time distributions were inspected to flag implausible submissions; no thresholds triggered exclusion.\u003c/p\u003e\n\u003ch3\u003eInstrument development and pretesting\u003c/h3\u003e\n\u003cp\u003eThe survey comprised six main sections assessing: (i) sociodemographic and social characteristics, (ii) health status and self-care practices; (iii) sources of health information, frequency of use, and trust; (iv) exposure to misleading health content and recognition of misinformation tactics; (v) health literacy and numeracy; and (vi) behavioural responses to misinformation and conflicting claims.\u003c/p\u003e \u003cp\u003eNumeracy was assessed using items on the ability to interpret simple risk statistics (e.g., \"1 in 10\") and read medicine labels correctly, both rated on five-point scales (strongly disagree to strongly agree). Misinformation recognition was measured across five common tactics: sensational headlines, cherry-picked evidence, fake experts or credentials, AI-generated images or videos, and impersonation of official sources, each rated on a five-point frequency scale (never to very often). Trust in information sources was measured on five-point scales (not at all to completely) across professional sources (doctors, nurses, pharmacists), institutional sources (NHS, government/Ministry of Health websites, WHO), and non-professional sources (social media platforms including Facebook, YouTube, TikTok; news media; influencers; and AI-based tools). Self-care practices were assessed by self-reported use of prescription medicines, over-the-counter medicines, supplements and herbal products in the preceding three months, and maintenance of a basic home care kit. Barriers to and benefits of self-care were measured as endorsements of predefined items (cost, time, information overload, distrust of sources, cultural beliefs and availability of services for barriers; autonomy, disease prevention, faster symptom relief, fewer clinic visits, and lower cost for benefits). The questionnaire underwent internal piloting to refine wording and routing logic before launch. A copy of the survey is included in \u003cb\u003eSupplementary file 2\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePage design, adaptive questioning, completeness checks and timing\u003c/h2\u003e \u003cp\u003eThe survey used multiple pages with a visible progress indicator. Limited branching logic was applied: detailed questions about the frequency of use of specific health information sources were displayed only to those who reported any use of those sources; similarly, detailed items about specific misinformation tactics were displayed only to those who reported exposure to misleading health content in the preceding six months. Key outcomes (e.g., perceived ability to recognise misinformation, delayed care-seeking, trust ratings, self-care practices) were set as required items, with \"Prefer not to say\" offered for sensitive demographics. The platform enforced completeness checks before advancing between pages. Consistent with CHERRIES, we implemented post-hoc checks for potential duplicate participation. IP addresses were inspected for repetition.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcomes and measures\u003c/h3\u003e\n\u003cp\u003ePrimary outcomes were participants' perceived ability to recognise misleading or inaccurate health information (rated on a five-point scale from \"not at all true for me\" to \"completely true for me\") and frequency of delayed professional care-seeking following exposure to misleading health content (rated as never, rarely, sometimes, often, or very often). Secondary outcomes included frequency of exposure to false or misleading health content in the preceding six months (never, once or twice, monthly, weekly, or daily), recognition of specific misinformation tactics (sensational headlines, cherry-picked evidence, fake experts, AI-generated images or videos, and impersonation of official sources), trust in various information sources (professional, institutional, social media, news media, influencers, and AI tools), self-care engagement and confidence, perceived barriers to and benefits of self-care, and health literacy. Additional behavioural outcomes included scenario-based responses to hypothetical misinformation exposures on topics such as vaccine safety, weight-loss medicines, herbal remedies, AI-generated content, experimental treatments, and celebrity health claims. All item texts and coding rules are provided in the Supplement and in the statistical analysis plan.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics summarised participant sociodemographic characteristics, health indicators, self-care practices, information sources, trust in sources, and exposure to misleading health content using frequencies, percentages, mean and standard deviation. For ordinal or Likert-type items, full distributions were presented. Bivariate associations were explored using Spearman's rank correlation (non-parametric test) for ordinal variables. Associations between numeracy and recognition of specific misinformation tactics were assessed using Spearman's correlation coefficients, with Bonferroni correction applied to adjust for multiple comparisons. The association between perceived erosion of confidence from conflicting claims and overall confidence in self-care decision-making was also assessed using Spearman's correlation coefficient.\u003c/p\u003e \u003cp\u003eMultivariable analyses employed ordinal logistic regression to estimate unadjusted and adjusted odds ratios (ORs and aORs) with 95% confidence intervals (CIs). The first set of models examined predictors of participants' perceived ability to recognise misleading health information, including educational attainment, age, sex, ethnicity, urbanicity, and participation in community or religious activities. The second set assessed predictors of delayed professional care-seeking, with a focus on frequency of exposure to false or misleading health content alongside demographic covariates. Fully adjusted models included all key sociodemographic variables, and robust standard errors were computed to account for potential heteroscedasticity. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, with Bonferroni correction applied where multiple comparisons were made. All analyses were performed using STATA, version 18 (StataCorp LP, College Station, TX, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEthics, consent and data protection\u003c/h2\u003e \u003cp\u003e The study received a favourable opinion from Imperial College Research Ethics Committee (ICREC #8061247) and complied with UK GDPR. Participants viewed an information sheet and provided electronic informed consent before answering any items; consent pages described purpose, voluntary nature, data handling and contact details. No direct personal identifiers were collected. IP addresses and coarse location, captured by the platform, were accessible only to the core research team for fraud and duplication checks and were not used to reidentify participants. Data were stored on secure institutional servers and analysed in deidentified form.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePatient and public involvement\u003c/h2\u003e \u003cp\u003eThe survey was developed by the research team and after several iterations, the instrument was passed on to three lay members for comments. The survey was iterated again among the research team partners to arrive at the final version.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSample characteristics\u003c/h2\u003e \u003cp\u003eA total of 1,414 adults residing in the UK completed the survey. The main survey findings are included in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e. Participant characteristics are illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of participants was 46.8 years (SD 15.8; range 18\u0026ndash;88), with a near-equal distribution by sex (51.8% female, 47.7% male). Almost half of the respondents lived in urban areas (46.1%), with the remainder residing in peri-urban (33.7%) or rural settings (20.2%). The sample was predominantly White (84.9%), with smaller proportions identifying as Asian (7.4%), Black (3.5%), Mixed (2.2%), or Other ethnic backgrounds (1.1%).\u003c/p\u003e \u003cp\u003eSocioeconomic indicators suggested a largely economically active population: 67.4% were in paid employment, 15.1% were retired and 4.5% were unemployed. Educational attainment was high, with 59.3% reporting university or postgraduate qualifications. Despite this, financial strain was evident; 17.9% reported sometimes or often skipping essentials due to cost and only 16.2% reported holding private health insurance.\u003c/p\u003e \u003cp\u003eSocial capital indicators were mixed. While most participants reported having at least one person they could rely on for support in a health crisis, 7.3% reported having no such support. More than half never participated in community or religious activities and loneliness was reported at least \u0026ldquo;some of the time\u0026rdquo; by 52.0% of respondents. Around 41% reported living with at least one long-term health condition, most commonly mental health or physical conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics (N\u0026thinsp;=\u0026thinsp;1,414)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (Percentage)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhat is your age?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.8 (15.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.0\u0026ndash;88.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhat sex were you assigned at birth?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e732 (51.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e675 (47.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrefer not to say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIn which country do you currently live?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1414 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIn what type of area do you live in?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e476 (33.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e652 (46.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHow would you describe your ethnic or cultural identity?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1201 (84.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrefer not to say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhat is your current main work status?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed full-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e702 (49.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed part-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e251 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomemaker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnable to work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIf employed, what type of work do you do?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture/fishing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional/technical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e638 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eService/sales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (11.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkilled manual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnskilled labour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e387 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHow religious or spiritual would you describe yourself?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot at all religious or spiritual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e772 (54.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlightly religious or spiritual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately religious or spiritual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159 (11.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery religious or spiritual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtremely religious or spiritual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrefer not to say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhat is the highest level of education you have completed?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVocational/technical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e277 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e533 (37.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e305 (21.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrefer not to say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDo you sometimes skip essentials (food, utilities, healthcare) because of cost?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e777 (55.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e384 (27.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e205 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery often\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDo you have private health insurance?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e229 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1185 (83.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHow many people can you rely on for support in a health crisis?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e701 (49.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e488 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHow often do you participate in community or religious group activities?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e783 (55.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e335 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178 (12.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery often\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHow often do you feel lonely?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e298 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHardly ever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e380 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome of the time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e361 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e273 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften / Always\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDo you live with a long-term disability or health condition?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e241 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e837 (59.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrefer not to say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhat is your migration status?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorn in your current country of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1225 (86.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmigrant\u0026thinsp;\u0026le;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmigrant\u0026thinsp;\u0026gt;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrefer not to say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHow well do you read in the survey language?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot at all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative/Fluent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1363 (96.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHealth status and self-care practices\u003c/h2\u003e \u003cp\u003eSelf-rated health was generally favourable, with 64.3% reporting good or very good health, although 35.7% rated their health as fair or poor. Over half of respondents (54.7%) reported no chronic conditions (\u003cb\u003eSupplementary Table\u0026nbsp;1)\u003c/b\u003e. Among those with long-term conditions, mental health conditions (20.4%) and hypertension (11.9%) were most frequently reported.\u003c/p\u003e \u003cp\u003eEngagement in self-care using pharmaceutical and other products was widespread. In the preceding three months, 55.9% had used prescription medicines, 60.0% had used over-the-counter medicines and 26.5% had used supplements or herbal products. The majority (90.2%) reported maintaining a basic home care kit.\u003c/p\u003e \u003cp\u003eConfidence in self-care was high. Nearly all respondents agreed or strongly agreed that they could manage minor ailments at home (98.3%), knew when to seek professional care (92.9%) and knew where to find trustworthy self-care guidance (91.4%). Self-care was endorsed as a first step before consulting a healthcare professional by 92.0% of participants and 78.0% agreed or strongly agreed that they practised self-care regularly.\u003c/p\u003e \u003cp\u003ePerceived benefits of self-care included fewer clinic visits (66.0%) as delay in these instances may be deemed appropriate, faster symptom relief (61.2%) and disease prevention (51.3%). However, barriers were also common, particularly cost (40.1%), time constraints (33.4%), information overload (30.2%) and distrust of information sources (31.2%). Just over one-quarter (26.1%) reported no barriers to practising self-care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eInformation sources, trust and exposure to misleading health content\u003c/h2\u003e \u003cp\u003eProfessional and institutional sources dominated health information use and trust. The National Health Service was frequently consulted (60.9% often or very often) and highly trusted (76.4% very or complete trust), as were doctors (40.3% often/very often used; 73.7% very or completely trusted), nurses (26.8%; 64.6%), and pharmacists (27.5%; 60.9%); \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e. Universities and research institutes were less frequently consulted (12.5% often/very often) but remained moderately trusted (51.3% very or completely).\u003c/p\u003e \u003cp\u003eIn contrast, social media platforms and influencers were rarely used and widely distrusted. Most respondents reported never using Facebook (78.4%), TikTok (80.8%), or influencers (80.3%) for health information, and over 80% reported no trust at all in these sources. News media were also poorly trusted, with only 4.2% reporting very or complete trust. Use of AI-based tools and chatbots was limited (12.1% often or very often used), and trust was low: 37.0% reported no trust and only 9.4% reported very or complete trust.\u003c/p\u003e \u003cp\u003eExposure to health misinformation was common. In the preceding six months, 81.8% of respondents reported encountering false or misleading health content at least once and 65.3% reported exposure to content they believed was deliberately false. The most frequently cited topics included nutrition and weight loss, supplements, vaccines and weight-loss medicines.\u003c/p\u003e \u003cp\u003eParticipants most frequently reported encountering sensational headlines (57.7% often or very often) and cherry-picked evidence (52.2%), followed by fake experts or credentials (33.8%). AI-generated images or videos (40.8%) and impersonation of official sources (17.5%) were also reported as occurring often or very often. Despite this high exposure, most respondents rated their ability to recognise misleading information as \u0026ldquo;mostly true\u0026rdquo; or \u0026ldquo;completely true\u0026rdquo; (51.8%), although confidence declined when participants reported encountering conflicting health claims.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEducational attainment and perceived ability to recognise misinformation\u003c/h2\u003e \u003cp\u003eOrdinal logistic regression examined sociodemographic predictors of self-reported ability to recognise misleading health information (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Higher educational attainment showed a strong, graded association with perceived recognition ability. Compared with secondary education, university education (adjusted odds ratio [aOR] 2.05, 95% CI 1.57\u0026ndash;2.68) and postgraduate education (aOR 2.29, 95% CI 1.69\u0026ndash;3.10) were associated with significantly higher odds of reporting greater recognition ability. Vocational or technical education showed no significant association.\u003c/p\u003e \u003cp\u003eUrbanicity was independently associated with perceived recognition ability. Participants living in rural (aOR 1.59, 95% CI 1.20\u0026ndash;2.11) or peri-urban areas (aOR 1.44, 95% CI 1.15\u0026ndash;1.80) reported higher recognition ability compared with urban residents; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Asian participants had substantially lower odds of reporting high recognition ability than White participants (aOR 0.39, 95% CI 0.26\u0026ndash;0.60), while no significant differences were observed for Black, Mixed, or Other ethnic groups. Increasing age was associated with lower perceived ability to recognise misinformation. Sex and community or religious participation were not significantly associated after adjustment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnadjusted and adjusted ordinal logistic regression models examining educational attainment, demographics and social predictors of self-reported ability to recognise health misinformation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAdjusted*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eHighest level of education completed\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u0026ndash;7.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u0026ndash;5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u0026ndash;21.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u0026ndash;19.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVocational/technical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026ndash;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u0026ndash;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55\u0026ndash;2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.57\u0026ndash;2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62\u0026ndash;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.69\u0026ndash;3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95\u0026ndash;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32\u0026ndash;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.26\u0026ndash;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u0026ndash;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u0026ndash;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u0026ndash;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u0026ndash;3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u0026ndash;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrbanicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13\u0026ndash;1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20\u0026ndash;2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u0026ndash;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.15\u0026ndash;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity/religious activities participation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026ndash;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71\u0026ndash;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63\u0026ndash;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u0026ndash;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u0026ndash;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery often\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u0026ndash;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47\u0026ndash;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e *Fully adjusted model includes age, sex, ethnicity, education, urbanicity and community/religious participation. Robust standard errors used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eExposure to misleading information and delays in professional care-seeking\u003c/h2\u003e \u003cp\u003eJust over half of respondents (51.6%) reported never delaying professional care due to online health claims, while 30.8% reported rare delays and 17.6% reported sometimes or more frequent delays. In adjusted ordinal logistic regression, exposure to misleading health content showed a clear dose-response relationship with delays in care-seeking.\u003c/p\u003e \u003cp\u003eUnadjusted and adjusted ordinal logistic regression models examining exposure to false/misleading health content, educational attainment and demographics as predictors of delays in professional care seeking are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Compared with respondents reporting no exposure, those exposed once or twice had higher odds of delay (aOR 1.53, 95% CI 1.11\u0026ndash;2.12). Monthly exposure was associated with further increased odds (aOR 1.83, 95% CI 1.27\u0026ndash;2.63) and weekly exposure showed the highest risk (aOR 1.89, 95% CI 1.31\u0026ndash;2.71). Daily exposure was not significantly associated with additional risk.\u003c/p\u003e \u003cp\u003eOlder age was associated with lower odds of delay. Asian (aOR 2.35, 95% CI 1.63\u0026ndash;3.40) and Black participants (aOR 1.86, 95% CI 1.18\u0026ndash;2.91) had significantly higher odds of delaying professional care compared with White participants. Peri-urban residence was associated with lower odds of delay compared with urban residence. Educational attainment and sex were not independently associated with delays after adjustment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnadjusted and adjusted ordinal logistic regression models examining exposure to false/misleading health content, educational attainment and demographics as predictors of delays in professional care seeking\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAdjusted*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eFalse/misleading health content exposure (past 6 months)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce/twice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28\u0026ndash;2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11\u0026ndash;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70\u0026ndash;3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.27\u0026ndash;2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58\u0026ndash;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.31\u0026ndash;2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026ndash;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u0026ndash;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHighest level of education completed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74\u0026ndash;16.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u0026ndash;46.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u0026ndash;3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.10\u0026ndash;5.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVocational/technical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026ndash;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u0026ndash;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026ndash;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u0026ndash;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026ndash;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u0026ndash;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u0026ndash;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u0026ndash;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u0026ndash;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13\u0026ndash;4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.63\u0026ndash;3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u0026ndash;3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18\u0026ndash;2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u0026ndash;3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63-3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u0026ndash;7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00-6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrbanicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u0026ndash;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51\u0026ndash;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56\u0026ndash;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eNumeracy and recognition of misinformation tactics\u003c/h2\u003e \u003cp\u003eSpearman\u0026rsquo;s rank correlation analyses assessed associations between numeracy skills and recognition of common misinformation tactics, with Bonferroni correction applied (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Numeracy showed robust positive associations with recognition of sensational headlines (ρ\u0026thinsp;=\u0026thinsp;0.156, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and cherry-picked evidence (ρ\u0026thinsp;=\u0026thinsp;0.158, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), both surviving correction for multiple comparisons.\u003c/p\u003e \u003cp\u003eNo meaningful associations were observed between numeracy and recognition of AI-generated images or videos, or impersonation of official sources. Recognition of fake experts or credentials showed a weak association that did not survive correction. These findings indicate that numeracy skills were associated with detection of argument-based misinformation but not with technologically mediated or identity-based deception.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman's rank correlation between numeracy skills and recognition of misinformation tactics: analysis with Bonferroni multiple comparisons correction (α\u0026thinsp;=\u0026thinsp;0.01)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMisinformation Tactic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman's rho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvives Bonferroni Correction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensational headlines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobust positive association: Higher numeracy significantly predicts better recognition of sensational framing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCherry-picked evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobust positive association: Strongest correlation observed; numeracy is most predictive of detecting selective evidence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFake experts or credentials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo (p\u0026thinsp;\u0026gt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpurious positive: Appears significant at the conventional level but is a false positive after correction for multiple testing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI-generated images/videos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo association: Numeracy skill unrelated to recognising AI-generated content; statistical literacy does not transfer to technological deception detection\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpersonation of\u003c/p\u003e \u003cp\u003eofficial sources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo association: Weak negative trend, not statistically significant; numeracy does not predict detection of identity-based manipulation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBehavioural responses to health misinformation\u003c/h2\u003e \u003cp\u003eBehavioural responses following exposure to health misinformation were predominantly cautious. Most respondents reported verifying information before acting, relying on official sources, or seeking professional advice; \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e. However, potentially risky behaviours were not uncommon. Nearly half of respondents reported substituting self-care for professional consultation at least sometimes following exposure, whereas 13.9% reported sometimes or more frequent cessation of medicines without further advice. Scenario-based items consistently showed low endorsement of believing or sharing unverified claims, with most respondents indicating that they would verify information, consult trusted sources, or ignore misleading content.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eConflicting claims and confidence in self-care decision-making\u003c/h2\u003e \u003cp\u003eAlmost half of the respondents agreed that encountering conflicting health claims reduced their confidence in self-care decisions. Spearman\u0026rsquo;s correlation analysis demonstrated a significant negative association between agreement that conflicting claims reduce confidence and overall confidence in self-care decision-making (ρ= \u0026minus;0.142, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that perceived informational conflict was associated with lower self-care confidence (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eSummary of principal findings\u003c/h2\u003e \u003cp\u003eThis study provides a comprehensive population-level examination of self-care practices, health information use, trust and exposure to pharmaceutical misinformation within a contemporary digital ecosystem in the UK. Several findings are particularly salient. First, engagement in self-care was widespread and confident as most respondents reported high capability in managing minor ailments, strong knowledge of when to seek professional care and routine use of prescription, over-the-counter and supplementary products. Trust in professional and institutional sources, especially the NHS, doctors, nurses and pharmacists, remained high, despite frequent exposure to misleading health content.\u003c/p\u003e \u003cp\u003eSecond, exposure to false or misleading pharmaceutical and health information was pervasive. Over four-fifths of respondents reported encountering misleading content in the preceding six months, commonly related to nutrition, supplements, vaccines and weight-loss medicines. Recognition of common misinformation tactics such as sensational headlines and cherry-picked evidence was widespread, yet confidence declined when individuals were confronted with conflicting claims.\u003c/p\u003e \u003cp\u003eThird, educational attainment emerged as a strong and independent predictor of perceived ability to recognise misinformation, with a clear gradient favouring university and postgraduate education. However, this apparent \u0026ldquo;literacy advantage\u0026rdquo; did not uniformly translate into behavioural protection. Exposure to misleading content was associated with a graded increase in delays in professional care-seeking, even after adjustment for education, age, sex, ethnicity and urbanicity. Notably, higher exposure frequencies showed diminishing marginal effects, suggesting a possible saturation phenomenon.\u003c/p\u003e \u003cp\u003eFourth, inequities were evident as Asian and Black participants had significantly higher odds of delaying professional care following exposure to misleading information, while urban residents reported both lower perceived recognition ability and higher likelihood of delay compared with peri-urban and rural respondents. These patterns highlight that susceptibility to misinformation is not solely a function of individual cognitive skill but is socially and contextually patterned.\u003c/p\u003e \u003cp\u003eFifth, health literacy skills showed differentiated effects. Numeracy was associated with improved recognition of argument-based misinformation tactics (sensational framing and selective evidence), but not with detection of technologically mediated deception such as AI-generated imagery or impersonation of official sources. This dissociation highlights a critical gap between traditional health literacy competencies and emerging forms of digital manipulation. However, delayed professional care-seeking should not be interpreted uniformly as inappropriate or unsafe behaviour. In the context of self-care for common, self-limiting conditions, a period of watchful waiting is explicitly endorsed within UK guidance. NHS and NICE advice encourages individuals to manage minor illness at home and to seek professional input only if symptoms persist beyond their expected natural course or worsen. For example, NHS guidance advises that an acute cough may reasonably last up to three weeks before medical review is required, while sore throat symptoms typically resolve within around one week (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Clear communication about normal symptom duration has been a cornerstone of national strategies to reduce unnecessary consultations for minor illness. In this regard, these findings therefore challenge the implicit assumption that all care-seeking delay reflects misinformation-related harm or poor decision-making. Instead, they highlight the need to distinguish between appropriate, guideline-concordant delay in self-limiting conditions and potentially harmful delay in situations where professional assessment is clinically indicated.\u003c/p\u003e \u003cp\u003eFinally, the analysis distinguishes between argument-based misinformation tactics (such as sensational headlines and cherry-picked evidence) and technologically mediated or identity-based tactics (including AI-generated content and impersonation of official sources). This distinction reflects emerging concerns that the evolving nature of health misinformation may outpace traditional educational and literacy-based countermeasures. In doing so, the study contributes empirically to ongoing debates about whether current public health strategies, largely focused on individual capability-building, are sufficient to safeguard timely access to care in a rapidly changing information environment. Together, this work positions misleading health information as not only a challenge to knowledge accuracy but also a potential patient safety and access issue within routine healthcare systems. Understanding its behavioural implications is essential for designing proportionate, equity-sensitive responses that support effective self-care while preserving trust in professional care pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eComparison with existing literature\u003c/h2\u003e \u003cp\u003eThe high prevalence of exposure to misleading health information observed in this study aligns with international evidence describing the normalisation of misinformation within everyday digital consumption. Prior work has consistently shown that false or misleading health content circulates widely and is often encountered incidentally rather than sought deliberately (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). The present findings extend this literature by demonstrating that such exposure is now near-ubiquitous even in a highly educated UK sample with strong trust in professional health institutions.\u003c/p\u003e \u003cp\u003eThe association between misinformation exposure and delayed care-seeking corroborates earlier experimental and observational studies showing that misinformation can influence not only beliefs but downstream health behaviours (\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Importantly, this study moves beyond vaccination-specific contexts to demonstrate similar behavioural risks across a broader pharmaceutical and self-care landscape, including medicines use, supplements and chronic disease claims. The observed dose\u0026ndash;response relationship reinforces concerns that cumulative exposure may incrementally erode timely engagement with formal healthcare services.\u003c/p\u003e \u003cp\u003eEducational gradients in misinformation recognition mirror well-established health literacy research, which consistently links higher educational attainment to greater confidence and competence in navigating health information. However, the absence of a protective effect of education against care-seeking delays is notable (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). These findings challenge implicit assumptions that improving factual knowledge or critical appraisal skills alone will suffice to mitigate behavioural harms. Instead, it supports emerging evidence that even highly literate individuals may experience decisional paralysis, mistrust, or delay when confronted with conflicting or emotionally salient information (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe urban disadvantage observed in both perceived recognition ability and care-seeking behaviour contrasts with common narratives that associate urban residence with superior access to information and services (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). This pattern may reflect higher exposure intensity, greater information overload, or more fragmented trust environments in densely networked urban settings. While speculative, this interpretation is consistent with sociological accounts of \u0026ldquo;information saturation\u0026rdquo; and warrants closer investigation (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe differential associations by ethnicity are consistent with broader literature documenting structural and historical drivers of mistrust, differential exposure pathways and unequal health system experiences among minority ethnic groups (\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Importantly, these findings should not be interpreted as evidence of intrinsic vulnerability, but rather as signals of uneven informational and institutional environments that shape how misinformation is encountered and acted upon.\u003c/p\u003e \u003cp\u003eFinally, the selective association between numeracy and recognition of specific misinformation tactics aligns with cognitive psychology research distinguishing analytic reasoning from perceptual or identity-based deception detection (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Traditional health literacy frameworks have emphasised statistical understanding and evidence appraisal; however, the present findings suggest these skills offer limited protection against synthetic media and impersonation tactics increasingly enabled by generative AI (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eImplications for research and practice\u003c/h2\u003e \u003cp\u003eFirst, these findings highlight that trust in professional and institutional sources remains a critical protective asset. Efforts to counter pharmaceutical misinformation should therefore prioritise strengthening the visibility, accessibility and communicative clarity of trusted sources rather than relying solely on debunking false claims. Second, the observed behavioural impact of misinformation exposure, particularly delays in care, highlights the need to integrate misinformation risk explicitly into patient safety and access frameworks. Delayed presentation can have material consequences for disease progression, treatment effectiveness and health system costs. Monitoring misinformation-related delays should therefore be considered alongside more established quality and safety indicators.\u003c/p\u003e \u003cp\u003eThird, the findings suggest that conventional health literacy interventions may be necessary but insufficient. While numeracy and critical appraisal skills support the detection of certain misinformation forms, they do not equip individuals to identify AI-generated content or impersonation of authority. This points to the need for updated literacy models that incorporate digital provenance cues, platform dynamics and heuristic recognition of manipulation techniques.\u003c/p\u003e \u003cp\u003eFourth, the persistence of inequities by ethnicity and urbanicity indicates that universal messaging strategies are unlikely to be uniformly effective. Tailored, community-embedded approaches that acknowledge differential trust histories and exposure contexts are likely to be required. Pharmacists especially may represent an under-utilised intermediary given their high trust levels and accessibility.\u003c/p\u003e \u003cp\u003eFinally, the low trust in social media platforms, influencers and news media coupled with strong support for transparency, external verification and quality assurance, suggests public appetite for more visible governance mechanisms. While this study does not evaluate regulatory interventions directly, it supports the principle that trust-building features should be additive and explicit rather than assumed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study has several strengths. It draws on a large UK sample with broad demographic coverage and provides granular data across self-care behaviours, information sources, trust, exposure and behavioural responses. The inclusion of inferential analyses allows identification of independent predictors while controlling for key sociodemographic factors. The use of scenario-based items enhances ecological validity by approximating real-world decision contexts.\u003c/p\u003e \u003cp\u003eHowever, limitations should be acknowledged. The cross-sectional design precludes causal inference and associations between exposure and behaviour may be bidirectional. Self-reported measures of exposure, recognition ability and behaviour are subject to recall and social desirability biases. Educational attainment was high and the sample was predominantly White, which may limit generalisability to more socioeconomically disadvantaged or ethnically diverse populations. Perceived ability to recognise misinformation may not correspond to objective accuracy and future work should incorporate performance-based measures.\u003c/p\u003e \u003cp\u003eAdditionally, while the study captures a wide range of misinformation types, it does not disentangle specific platform effects or content features in detail. Nor does it directly assess clinical outcomes associated with delayed care. We also acknowledge the absence of data on respondents\u0026rsquo; affiliation with the health sector. Collecting such data would be helpful to understand more deeply the factors that influence how health misinformation is interpreted and evaluated. These gaps represent important avenues for future investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eFuture research\u003c/h2\u003e \u003cp\u003eFuture research should prioritise longitudinal designs to clarify temporal relationships between exposure, trust erosion and health behaviours. Linking survey data to healthcare utilisation records could help quantify the clinical and economic consequences of misinformation-related delays. Experimental studies are needed to test which trust-enhancing features, such as quality assurance labels, third-party verification, or transparent sourcing, most effectively mitigate behavioural harms.\u003c/p\u003e \u003cp\u003eFurther work should also examine how generative AI reshapes misinformation exposure and detection, particularly as synthetic media becomes more sophisticated. Developing and validating new literacy instruments that capture digital provenance skills will be essential. Finally, participatory research with minoritised and urban communities is needed to co-design interventions that address structural drivers of vulnerability rather than placing responsibility solely on individuals.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis UK study found widespread and confident self-care, particularly relating to the use of pharmaceuticals and other products, rooted in strong trust in professional sources, yet nearly universal exposure to pharmaceutical misinformation with clear behavioural impacts, including delayed care-seeking. Higher education improved perceived recognition but did not remove behavioural risk, and inequities persisted by ethnicity and urbanicity. Traditional literacy skills aided detection of some misinformation but were insufficient against AI-enabled tactics. These findings show that pharmaceutical misinformation represents a systemic challenge requiring equity-focused, system-level responses alongside individual literacy support.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAEO and AM are supported by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration Northwest London. The views expressed in this publication are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors provided substantial contributions to the conception (AEO, PS), design (AEO, SA, AA), acquisition (SA, AEO) and interpretation (AEO, SA, DG, SAN, PS, DS, AM) of the study data. AEO took the lead in planning the study with support from co-authors. SA carried out the data analysis and developed the manuscript which was later revised and approved by the co-authors. AEO is the guarantor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTwitter:\u0026nbsp;\u003c/strong\u003e@austenelosta @ImperialSCARU\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNutbeam D (2000) Health literacy as a public health goal: a challenge for contemporary health education and communication strategies into the 21st century. 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Soc Sci Med 329:116044\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Imperial College London","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":"Pharmaceutical misinformation, health misinformation, self-care, health literacy, eHealth literacy, care-seeking behaviour, patient safety, trust in health information, artificial intelligence, health equity","lastPublishedDoi":"10.21203/rs.3.rs-8824194/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8824194/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eGenerate population-level evidence on exposure to misleading health information and its behavioural implications within contemporary self-care.\u003c/p\u003e\u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eCross-sectional, self-administered online survey.\u003c/p\u003e\u003ch2\u003eSetting\u003c/h2\u003e \u003cp\u003eUnited Kingdom.\u003c/p\u003e\u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e1,414 adults (mean age 46.8 years) recruited via a closed online panel using quota sampling by age, gender and ethnicity.\u003c/p\u003e\u003ch2\u003eMain outcome measures\u003c/h2\u003e \u003cp\u003eSelf-reported exposure to false or misleading health information; perceived ability to recognise misinformation; delays in professional care-seeking following exposure. Secondary measures included self-care practices, trust in information sources and recognition of common misinformation tactics. Multivariable ordinal logistic regression examined predictors of recognition ability and care-seeking delays; Spearman correlations assessed associations between numeracy and recognition of misinformation tactics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eExposure to misleading health information was widespread: 81.8% of respondents reported encountering false or misleading content in the previous six months. Confidence in recognising misinformation was high overall, with strong educational gradients; university and postgraduate education were independently associated with higher perceived recognition ability. However, educational attainment did not protect against behavioural consequences. Exposure to misleading content showed a clear dose-response association to professional care-seeking, even after adjustment for age, sex, education, ethnicity and urbanicity. Compared with no exposure, odds of delay increased among those exposed once or twice (aOR 1.53), monthly (aOR 1.83) and weekly (aOR 1.89). Asian and Black participants had significantly higher odds of delay compared with White participants and urban residents had higher risk than peri-urban residents. Numeracy skills were positively associated with recognising sensational headlines and cherry-picked evidence, but not with detection of AI-generated or impersonated content.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePharmaceutical and health misinformation was widespread and linked to inappropriate care-seeking. Although higher education increased perceived recognition, it did not eliminate behavioural risk. Traditional health literacy skills helped identify some forms of misinformation but offered limited protection against AI-enabled deception. These findings position pharmaceutical misinformation as a patient safety and access issue requiring coordinated, equity-sensitive, system-level responses alongside individual literacy efforts.\u003c/p\u003e","manuscriptTitle":"Exposure to health misinformation, self-care confidence and delayed care-seeking among adults in the UK: a cross-sectional survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 11:46:48","doi":"10.21203/rs.3.rs-8824194/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":"c21953ee-b873-41ab-99d8-e037407d5342","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62537161,"name":"Health Policy"}],"tags":[],"updatedAt":"2026-02-10T11:46:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 11:46:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8824194","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8824194","identity":"rs-8824194","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}