Responsiveness to societal needs in postgraduate medical education: the role of accreditation
Bibliographic record
Abstract
BACKGROUND: Social accountability in medical education has been defined as an obligation to direct education, research, and service activities toward the most important health concerns of communities, regions, and nations. Drawing from the results of a summit of international experts on postgraduate medical education and accreditation, we highlight the importance of local contexts in meeting societal aims and present different approaches to ensuring societal input into medical education systems around the globe. MAIN TEXT: We describe four priorities for social responsiveness that postgraduate medical education needs to address in local and regional contexts: (1) optimizing the size, specialty mix, and geographic distribution of the physician workforce; (2) ensuring graduates' competence in meeting societal goals for health care, population health, and sustainability; (3) promoting a diverse physician workforce and equitable access to graduate medical education; and (4) ensuring a safe and supportive learning environment that promotes the professional development of physicians along with safe and effective patient care in settings where trainees participate in care. We relate these priorities to the values proposed by the World Health Organization for social accountability: relevance, quality, cost-effectiveness, and equity; discuss accreditation as a lever for change; and describe existing and evolving efforts to make postgraduate medical education socially responsive. CONCLUSION: Achieving social responsiveness in a competency-based postgraduate medical education system requires accrediting organizations to ensure that learning emphasizes relevant competencies in postgraduate curricula and educational experiences, and that graduates possess desired attributes. At the same time, institutions sponsoring graduate medical education need to provide safe and effective patient care, along with a supportive learning and working environment.
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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 | 0.121 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.007 | 0.011 |
| 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".