Evaluation of Health Literacy Levels and Associated Factors Among Patients with Acute Coronary Syndrome and Heart Failure in Qatar
Bibliographic record
Abstract
Purpose: To determine the prevalence of inadequate health literacy and its associated risk factors among patients with acute coronary syndrome (ACS) and/or heart failure (HF) in Qatar. Patients and Methods: This cross-sectional observational study was conducted among patients with ACS and/or HF attending the national Heart Hospital in Qatar. Health literacy was assessed using the abbreviated version of the Test of Functional Health Literacy in Adults (S-TOFHLA) and the Three-item Brief Health Literacy Screen (3-item BHLS). Results: Three hundred patients with ACS and/or HF, majority male (88%) and non-Qatari (94%), participated in the study. The median (IQR) age of the participants was 55 (11) years. The prevalence of inadequate to marginal health literacy ranged between 36% and 54%. There were statistically significant differences in health literacy level between patients based on their marital status (p=0.010), education (p≤0.001), ability to speak any of Arabic, English, Hindi, Urdu, Malayalam, or other languages (p-values ≤0.001 to 0.035), country of origin (p≤0.001), occupation (p≤0.001), and receiving information from a pharmacist (p=0.008), a physiotherapist (p≤0.001), or a nurse (p=0.004). Conclusion: Inadequate health literacy is common among patients with ACS and/or HF. This study suggests a need for developing strategies to assist healthcare professionals in improving health literacy skills among patients with ACS and HF. A combination of interventions may be needed to improve patients' understanding of their disease and medications, and ultimately overall health outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".