Malaysian Health Literacy: Scorecard Performance from a National Survey
Bibliographic record
Abstract
Health literacy is an indicator of a society's ability to make better health judgements for themselves and the people around them. This study investigated the prevalence of health literacy among Malaysian adults and provided an overall picture of the society's current health literacy status, which has not been previously assessed. The study also highlighted socio-demographic markers of communities with limited health literacy that may warrant future intervention. A population-based self-administered survey using the Health Literacy Survey Malaysian Questionnaire18 (HLS-M-Q18) instrument was conducted as part of the National Health Morbidity Survey 2019 in Malaysia. The nationwide survey utilized a two-staged stratified random sampling method. A sample of 9478 individuals aged 18 and above, drawn from the living quarter list, participated in the study. The health literacy score was divided into three levels; limited, sufficient, and excellent. Findings showed a majority of the Malaysian population had a sufficient health literacy level in all three domains-healthcare, diseases prevention and health promotion (49.1%, 44.2%, and 47.5%, respectively)-albeit leaning towards the lower end of the category with an average score of 35.5. The limited health literacy groups were prevalent among respondents with older age (68%), lower education level (64.8%), and lower household income (49.5%). The overall health literacy status for Malaysia was categorized at a lower sufficiency level. Future health literacy improvements should focus on communities with a limited health literacy level to improve the overall score.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".