Health literacy among Iranian adults: findings from a nationwide population-based survey in 2015
Bibliographic record
Abstract
BACKGROUND: Health literacy is one of the most important determinants of noncommunicable diseases prevention. Health literacy is associated with elevated risks for poorer access to care, adverse health outcomes, and increased hospitalization and health costs. AIMS: This study aimed to determine the level of health literacy among the general adult population in the Islamic Republic of Iran. METHODS: Using a cross-sectional study during 2014-2015 with a multistage cluster sampling approach, we administered a pilot-tested standardized questionnaire to assess different domains of health literacy (i.e., reading, comprehension, communication/decision-making and Interpretation/judgment skills, individual and social empowerment, health information access and health information use) among 8439 (3935 males) individuals aged 18-60 years. Data were collected through face-to-face interviews. Descriptive statistics and multivariable linear regression method using SPSS (20) were applied to identify the factors associated with health literacy among Iranian adults. RESULTS: The mean health literacy level was 10.2±3.8 (out of 20). Only 18% (95% confidence interval [CI]: 17.15-18.78) of the participants had adequate health literacy, while 45.7% (95% CI: 44.64-46.78) had inadequate, and the 36.3% (95% CI: 35.21-37.33) had moderate health literacy. In the adjusted linear regression model, education level (the smallest β = 4.35, P < 0.001), age (β = 0.01, P = 0.002), female sex (β = 0.45, P < 0.001), residency in rural areas (β = 0.26, P < 0.001) and having permanent job (β = 1.03, P < 0.001) were significantly associated with more health literacy. CONCLUSION: Our findings highlighted that the Iranian adult population has an insufficient level of health literacy, which calls for comprehensive education planning to improve the levels, with special attention to certain subpopulations (e.g. illiterate populations) and domains (e.g. individual empowerment).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".