Introducing a Collaborative E2 (Evaluation & Enhancement) Social Accountability Framework for Medical Schools
Bibliographic record
Abstract
Medical schools recognize that they have an important social mandate beyond their primary role to educate future physicians. The instantiation of social accountability (SA) within faculties of medicine requires intentional, effective partnering with diverse internal and external stakeholders. Despite early, promising academic work in the field of SA in medical education, there remains a lack of conceptual clarity about what SA could and should entail, and a lack of practical direction regarding how it could be implemented. The paper describes the development of an innovative SA framework that incorporates both pragmatic-evaluation and collaborative-enhancement components. The framework consists of five distinct phases, uses a deliberative engagement methodology, and is meaningfully informed by a set of four SA Lenses: Diversity, Inclusion and Cultural Responsiveness; Equity; Community / Stakeholder Engagement and Partnering; and Justice-Fairness and Sustainability. In addition to using the framework to evaluate and enhance the social accountability statuses of a variety of the medical school’s operational components, Dalhousie Faculty of Medicine leaders are committed to applying the framework’s SA Lenses to important decision-making processes, such as the revision of the medical school’s strategic directions and the allocation of limited resources to address important, emerging medical education issues and challenges.
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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 | 0.170 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.008 | 0.007 |
| 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".