A critical hybrid realist-outcomes systematic review of relationships between medical education programmes and communities: BEME Guide No. 35
Bibliographic record
Abstract
BACKGROUND: The relationships between medical schools and communities have long inspired and troubled medical education programmes. Successive models of community-oriented, community-based and community-engaged medical education have promised much and delivered to varying degrees. A two-armed realist systematic review was undertaken to explore and synthesize the evidence on medical school-community relationships. METHOD: One arm used standard outcomes criteria (Kirkpatrick levels), the other a realist approach seeking out the underlying contexts, mechanisms and outcomes. 38 reviewers completed 489 realist reviews and 271 outcomes reviews; 334 articles were reviewed in the realist arm and 181 in the outcomes arm. Analyses were based on: descriptive statistics on both articles and reviews; the outcomes involved; the quality of the evidence presented; realist contexts, mechanisms, and outcomes; and an analysis of underlying discursive themes. FINDINGS: The literature on medical school-community relationships is heterogeneous and largely idiographic, with no common standards for what a community is, who represents communities, what a relationship is based on, or whose needs are or should be being addressed or considered. CONCLUSIONS: Community relationships can benefit medical education, even if it is not always clear why or how. There is much opportunity to improve the quality and precision of scholarship in this area.
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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 | 0.180 | 0.297 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.035 | 0.019 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".