Achieving social accountability through interprofessional collaboration: The Canadian medical schools experience
Bibliographic record
Abstract
Social accountability in the health professions is increasingly recognized as a necessary foundation for delivering effective healthcare. Inter- and intra-professional collaboration is critical to the process in order to transform intent into action. This article outlines the three-year program undertaken by a national collaboration among all 17 Canadian medical schools and their partners as they engaged in a journey leading to the incorporation of social accountability in an interprofessional context as the cornerstone of healthcare education and practice. An overview of the various dimensions of this project is discussed in order to shed light on how a national initiative in collaboration with local initiatives can synergistically work toward a common goal. Successes and challenges in working on a national level are reviewed with implications for future directions for interprofessional collaboration in healthcare based upon principles and values of social accountability.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.074 | 0.019 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.020 |
| 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".