Applying quality and equity lenses to advance social accountability in medical education
Bibliographic record
Abstract
Social accountability has become a key driver of change within health professional education programs worldwide. Over the last two decades, there has been a growing number of frameworks, tools, and standards to help measure social accountability. This paper shares the experiencesand lessons learned from one of the institutions that participated in piloting the Institutional Self-Assessment Social Accountability Tool (ISAT). We argue that tools for measuring social accountability are valuable not only because they provide data but, more importantly, because they can embed a dialogic and critical reflective culture within institutions. We describe how the tool expanded our thinking about social accountability to advance it, including what constitutes socially accountable research and who and what fields contribute to this work. We argue that the tool encouraged collaborative critical reflexive practice and offered a process that was incorporated into institutional processes and activities, and simultaneously fostered intra-institutional cooperation. Finally, we contend that the application of quality and equity lenses offers sources of sustainability to advance social accountability in medical education as they encourage the prioritization of social accountability processes to achieve desired outcomes.
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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 | 0.205 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.016 | 0.071 |
| Scholarly communication | 0.029 | 0.033 |
| Open science | 0.004 | 0.049 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".