Leadership as a catalyst for advancing social accountability in health professions education: AMEE Guide No. 187
Bibliographic record
Abstract
Social accountability (SA) has become a key principle for health professions education, urging institutions to align their actions with the health priorities of the populations they serve. Despite its importance, many schools struggle to implement SA effectively, often due to fragmented leadership development and a lack of institutional frameworks. This AMEE Guide positions leadership as a strategic, system-wide driver of social accountability. Drawing on global case studies, established leadership models, and institutional evidence, it explains how leaders can translate SA from mission statements into measurable outcomes. The Guide introduces the SA values and links them to leadership practices. It offers practical recommendations across three levels: individual competencies, institutional mechanisms, and systemic enablers. The Guide highlights leadership models and demonstrates their application through detailed examples from institutions worldwide. By integrating leadership theory with practical strategies, the Guide provides a pathway for academic leaders to embed SA into their institutions' core practices through inclusive governance, responsive curricula, community partnerships, impact-driven research, and ongoing cultural change. It equips educators, deans, faculty developers, and policymakers with the tools needed to foster leaders capable of transforming medical and health professions education into a force for equity and social good.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".