A comparative study of community medicine and public health curriculum at medical schools in Iran and North America
Bibliographic record
Abstract
Background: Community medicine and public health are the core subjects in medical education. One of the main competencies of general physicians in the national curriculum is having knowledge and skills in health promotion and disease prevention in the health system. Any curriculum revision in community medicine departments needs to incorporate the evidence and use pioneer countries’ experiences in this issue. This study aims to compare community medicine and public health courses in medical schools between Iran and selected universities in North America. Methods: The elements of a community medicine curriculum for medical students were compared in a descriptive-comparative study using the Bereday model. These elements included objectives and competencies, educational strategies, teaching and learning methods, assessment, and educational fields in a community medicine curriculum in Iran and in selected universities in North America. A literature search was conducted in CINAHL, SCOPUS, MEDLINE, Web of Science, EBSCO, and on university websites. Results: Essential aspects of community-based strategies among community medicine and public health curriculum of general medicine in universities in Canada and the United States included a longitudinal approach, training in urban and rural primary care centers, teaching by family physicians and health center staff, a spiral curriculum, focus on social determinants of health, taking of social and cultural histories and social prescriptions, learning teamwork, and using LIC (Longitudinal Integrated Curriculum). Conclusion: The objective of community medicine and public health curriculum in selected North American universities was to prepare general practitioners who work in Level 2 and 3 hospitals and to improve their skills to provide high-quality services to the community. Some of the successful points in the selected universities that could be replicated in Iranian faculties of medicine included using integration strategy, a spiral curriculum, and an LIC approach.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".