Comparing and using prominent social accountability frameworks in medical education: moving from theory to implementation in Northern Ontario, Canada
Bibliographic record
Abstract
Background: Social accountability in medical education is conceptualized as a responsibility to respond to the needs of local populations and demonstrate impact of these activities. The objective of this study was to rigorously examine and compare social accountability theories, models, and frameworks to identify a theory-informed structure to understand and evaluate the impacts of medical education in Northern Ontario. Methods: Using a narrative review methodology, prominent social accountability theories, models, and frameworks were identified. The research team extracted important constructs and relationships from the selected frameworks. The Theory Comparison and Selection Tool was used to compare the frameworks for fit and relevance. Results: Eleven theories, models, and frameworks were identified for in-depth analysis and comparison. Two realist frameworks that considered community relationships in medical education and social accountability in health services received the highest scores. Frameworks focused on learning health systems, evaluating institutional social accountability, and implementing evidence-based practices also scored highly. Conclusion: We used a systematic theory selection process to describe and compare social accountability constructs and frameworks to inform the development of a social accountability impact framework for the Northern Ontario School of Medicine. The research team examined important constructs, relationships, and outcomes, to select a framework that fits the aims of a specific project. Additional engagement will help determine how to combine, adapt, and implement framework components to use in a Northern Ontario framework.
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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 | 0.100 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".