Proposing the Community Triad Model to action social accountability in medical schools
Bibliographic record
Abstract
This article is the third in a series exploring drivers of social accountability (SA) in medical schools across Canada. Findings from the two previous articles have highlighted a central relationship between community, students, and faculty at medical schools, and led to the emergence of a new social accountability model– the Community Triad Model (CTM). The CTM proposes an interconnectedness between community, students, faculty, and the broader institution, and the pathways through which community-based learning directly and indirectly influences decision-making in medical institutions. This article explores the relationships between the three arms of the CTM by examining the literature on community engagement and SA, as well as by revisiting popular models and foundational SA reports to garner insights into authentic community engagement in health professions education. While there is an abundance of literature demonstrating the impact of community placements on students, there are limited studies describing the influence of communities on faculty and the broader institution either directly, or indirectly via students. The authors recommend that institutions be more intentional in engaging students and faculty, and learn from their experiences with community to shape curriculum, practices, policies, and culture of the broader institution. This study offers an operational model of SA that is easy to adopt and implement. It intends to demonstrate how the components of the triad (students, faculty/leadership, community) function together in the community engagement and social accountability of 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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.016 | 0.065 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| 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".