Social Accountability Frameworks and Their Implications for Medical Education and Program Evaluation: A Narrative Review
Bibliographic record
Abstract
PURPOSE: Medical schools face growing pressures to produce stronger evidence of their social accountability, but measuring social accountability remains a global challenge. This narrative review aimed to identify and document common themes and indicators across large-scale social accountability frameworks to facilitate development of initial operational constructs to evaluate social accountability in medical education. METHOD: The authors searched 5 electronic databases and platforms and the World Wide Web to identify social accountability frameworks applicable to medical education, with a focus on medical schools. English-language, peer-reviewed documents published between 1990 and March 2019 were eligible for inclusion. Primary source social accountability frameworks that represented foundational values, principles, and parameters and were cited in subsequent papers to conceptualize social accountability were included in the analysis. Thematic synthesis was used to describe common elements across included frameworks. Descriptive themes were characterized using the context-input-process-product (CIPP) evaluation model as an organizational framework. RESULTS: From the initial sample of 33 documents, 4 key social accountability frameworks were selected and analyzed. Six themes (with subthemes) emerged across frameworks, including shared values (core social values of relevance, quality, effectiveness, and equity; professionalism; academic freedom and clinical autonomy) and 5 indicators related to the CIPP model: context (mission statements, community partnerships, active contributions to health care policy); inputs (diversity/equity in recruitment/selection, community population health profiles); processes (curricular activities, community-based clinical training opportunities/learning exposures); products (physician resource planning, quality assurance, program evaluation and accreditation); and impacts (overall improvement in community health outcomes, reduction/prevention of health risks, morbidity/mortality of community diseases). CONCLUSIONS: As more emphasis is placed on social accountability of medical schools, it is imperative to shift focus from educational inputs and processes to educational products and impacts. A way to begin to establish links between inputs, products, and impacts is by using the CIPP evaluation model.
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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 | 0.179 | 0.362 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".