Exploring social accountability in Canadian medical schools: broader perspectives
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. This article is the second of a two-part series in a study that explores key drivers of social accountability in Canada's medical schools and offers examples of social accountability in action. The study gathered perspectives from medical school staff, students and faculty through focus group discussions, using an appreciative inquiry approach. Drivers of social accountability emerging from the focus groups largely corroborate what was discovered in the first part of the series during key informant interviews with senior leaders. These include the importance of accreditation, leadership, vision and mandate, and community engagement among others, and highlight the key role champions play in driving social accountability. This study builds on the first article in the series by recognizing leadership as an important driver for social accountability, but highlighting how leadership alone is not enough. The broader range of perspectives gathered through the focus group discussions uncovered the importance of social accountability 'champions' at all levels: formal leadership, faculty, student and staff. Focus group discussions also uncovered an additional key driver that was not found in key informant interviews - cultural humility, with participants noting that action towards social accountability requires shifts not only in organizational structure, but also organizational culture, to foster real, lasting change. This study demonstrates the utility of an appreciative inquiry approach for understanding how complex systems like medical education institutions are innovatively tackling challenges around health equity. The richness of the themes that emerged consistently across focus group sessions and key informant interviews support the utility of the approach in furthering our understanding as to what is working to drive social accountability in some Canadian medical schools.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.066 | 0.035 |
| Scholarly communication | 0.025 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".