Characterizing health literacy in cardiac rehabilitation patients: a decade of multinational data (2014–2024)
Bibliographic record
Abstract
Background Cardiovascular disease (CVD) is a leading global health issue, with a high prevalence and significant economic impact. Cardiac rehabilitation (CR) can help mitigate this burden through its comprehensive approach, which includes, among other components, patient education. Health literacy, the ability to access and understand health information, is crucial for effective CR and better health outcomes. However, data on health literacy levels among CR patients across different countries is limited.Aims This study aimed to assess health literacy levels among CR patients from multiple countries and examine how these levels relate to various demographic and clinical characteristics. The goal was to provide insights that could help clinicians tailor their CR programs to better meet patient needs.Methods We conducted a cross-sectional analysis using baseline data from CR programs in Brazil, Canada, Colombia, Costa Rica, Peru, Spain, and the Philippines, collected between 2014 and 2024. Health literacy was measured using the Medical Term Recognition Test and the BRIEF Health Literacy Screening Tool. Descriptive statistics, chi-square tests, and logistic regression models were used to analyze the data and explore associations between health literacy and various conditions.Results Data from 1,491 patients revealed that approximately two-thirds had marginal (48.6%) or inadequate (13.5%) health literacy. Younger patients and those with lower educational attainment and income levels had higher rates of limited/marginal health literacy, while older individuals and those with higher educational levels or incomes showed better health literacy. Significant ethnic disparities were observed, with people from Southeast and South Asian exhibiting lower health literacy levels compared to White/European respondents. Marginal and inadequate health literacy were linked to higher rates of obesity and type 2 diabetes.Discussion This study is the first to provide a comprehensive assessment of health literacy among CR patients across multiple countries. The findings highlight the need for CR programs to incorporate broader educational strategies that address varying health literacy levels. The observed ethnic, age, and socioeconomic disparities suggest that CR programs should consider these factors when designing interventions. Future research should focus on longitudinal studies to further explore the relationship between CR participation and health literacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".