A Review of Global Health Information Literacy Policies and Recommendations for Iran
Bibliographic record
Abstract
Background. Health information literacy is a fundamental capability that enhances informed decision-making regarding individual and community health. Despite its importance in global health system policymaking, this domain in Iran faces significant structural and content-related challenges. This study aims to analyze and compare health information literacy policies in Iran with those of selected countries and to provide a concise summary of local policy through an analytical–comparative approach. Methods. This study, employing an analytical-comparative approach, presents a policy review that provides a narrative summary of local health information policies in Iran and selected countries. The data used in this study include national and international policy documents, scientific articles indexed in Scopus, PubMed, Web of Science, and SID databases, reports from international organizations such as WHO and OECD, and health policy resources from selected countries (Australia, Canada, the United States, and the European Union). Results. In Iran, policymaking related to health information literacy education is primarily shaped within three major institutions: The Ministry of Health, the Ministry of Education, and the national media (IRIB). However, efforts within these institutions have been fragmented and inconsistent, lacking a unified and comprehensive national strategy. This fragmentation has led to isolated initiatives without institutional coordination. Conclusion. Health information literacy in Iran requires a fundamental shift in the policymaking approach to overcome current fragmentation and move toward an integrated policy framework. International experiences demonstrate that effective policymaking in this area depends on coordinated interaction among health institutions, education systems, media, and civil society. Adopting cohesive and strategic policies can improve public health outcomes and reduce health inequalities in Iran.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".