Community engagement by faculties of medicine: A scoping review of current practices and practical recommendations
Bibliographic record
Abstract
PURPOSE: Social accountability (SA) is the responsibility of faculties of medicine (FoMs) to address the health priorities of the communities they serve. Community engagement (CE) is a vital, but often ambiguous, component of SA. Practical guidance on how to engage community partners (CPs) is key for meaningful CE. We conducted a systematic scoping review of CE involving FoMs to map out how FoMs engage their communities, to provide practical recommendations for FoMs to take part in CE, and to highlight gaps in the literature. MATERIALS AND METHODS: We searched electronic databases for articles describing projects or programs involving FoMs and CPs. Descriptive information was analyzed thematically. RESULTS: Thirty-eight of 1406 articles were included, revealing three themes: (1) Partners (Who to Engage)-deciding who to engage establishes the basis for responsibility and creates space for communities to engage FoMs; (2) Partnerships (How to Engage)-fostering creative and authentic collaboration, enabling meaningful community contributions; and (3) Projects and Programs (With What to Engage)-identifying opportunities for communities to have a voice in many spaces within FoMs. Under these themes emerged 32 practical recommendations. CONCLUSION: Practical guidance facilitates meaningful commitments to communities. The literature is rich with examples of community-FoM partnerships. We provide recommendations for CE that are clear, evidence-based, and responsive.
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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 | 0.101 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.038 | 0.036 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".