Integrating Public Health Into Undergraduate Medicine in North America: A Systematic Review
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has served as a stark reminder of the importance of foundational public health training for all physicians. However, the most effective way to incorporate these concepts into undergraduate medical education remains unclear. Here, we characterize the literature regarding the effectiveness of public health integration into undergraduate medical education in North America. We systematically searched MEDLINE, Embase, Cochrane Central, and Education Resources Information Center (ERIC) in accordance with preferred reporting items for systematic review and meta-analysis (PRISMA) guidelines for North American peer-reviewed literature, published from 01/01/2000 to 30/08/2021, that described outcomes of integrating public health training within an undergraduate medical curriculum. Results were qualitatively synthesized into key themes. A total of 38 studies, involving interventions across 43 medical schools, were included. Studies reported on a combination of public (n=13), global (n=9), population (n=9), community (n=6), and epidemiological (n=1) health interventions, and either implemented one-off workshops, electives, or international experiences (n=19); a longitudinal theme or long-term enrichment pathway (n=14); or a case-based learning curriculum (n=8). The majority (81.5%, 31/38) of integrations were self-described as successful and, of studies reporting on feasibility, most (94.1%, 16/17) were indicated as feasible. The definition of what constituted such success, however, was unclear. Innovative examples included the use of simulation workshops and mobile-optimized media content. Key challenges were noted, however, in securing adequate funding and buy-in from administrative leadership. Robust community partnerships and iterative cycles of implementation of the intervention were critical factors to success. In summary, foundational public health components can be effectively integrated into medical school curricula and would benefit from adequate resourcing, innovation, community partnerships, and continuous improvement.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".