Putting communities in the driver's seat: the realities of community-engaged medical education
Bibliographic record
Abstract
"Community" has featured in the discourse about medical education for over half a century. This discourse has explored relationships between medical education programs and communities in community-oriented medical education and community-based medical education and, in recent years, has extended to community-engaged medical education (CEME). This Perspective explores the developing focus on "community" in medical education, describes CEME as a concept, and presents examples of CEME in action at Flinders University School of Medicine (Australia), the Northern Ontario School of Medicine (Canada), and Ateneo de Zamboanga University School of Medicine (Philippines). The authors describe the ways in which CEME, which features active community participation, can improve medical education while meeting community needs and advancing national and international health equity agendas. They suggest that CEME can redefine student learning as taking place at the center of the partnership between communities and medical schools. They also consider the challenges of CEME and caution that criteria for community engagement must be sensitive to cultural variations and to the nature of the social contract in different sociocultural settings. The authors argue that CEME is effective in producing physicians who choose to practice in rural and underserved areas. Further research is required to demonstrate that CEME contributes to improved health, and ultimately health equity, for the populations served by the medical school.
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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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.032 | 0.078 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".