Enhancing Social Accountability in Medical Education and Accreditation: A Meeting Report
Bibliographic record
Abstract
Cynthia L Larche,1 Maxwell Kennel,1 Sean Tackett,2 David C Marsh,3– 5 Erin Cameron1,6 1Gilles Arcand Centre for Health Equity, Human Sciences Division, Northern Ontario School of Medicine University, Sudbury, Ontario, Canada; 2Division of General Internal Medicine, Johns Hopkins Bayview Medical Center, Baltimore, Maryland, USA; 3Clinical Sciences Division, Northern Ontario School of Medicine University, Sudbury, Ontario, Canada; 4Health Sciences North Research Institute, Sudbury, Ontario, Canada; 5ICES North, Sudbury, Ontario, Canada; 6Human Sciences Division, Northern Ontario School of Medicine University, Thunder Bay, Ontario, CanadaCorrespondence: Erin Cameron, Northern Ontario School of Medicine University, Thunder Bay, Ontario, Canada, Email ercameron@nosm.caAbstract: Accreditation in medical education stands in need of more empirical grounding. There is a paucity of accreditation research that hinders both quality assurance efforts and the introduction of innovative new approaches to accreditation, such as social accountability standards. The International Social Accountability and Accreditation Steering Committee (ISAASC) was established to address this gap. This meeting report outlines the outcomes of a workshop led by members of the ISAASC to identify research priorities related to social accountability and accreditation. The workshop was held during the International Congress on Academic Medicine (ICAM) on April 13, 2024, in Vancouver, British Columbia and used a modified nominal group technique. Four main priorities were identified, namely that there is a need to: 1) integrate various constituencies in accreditation research, 2) identify global drivers of social accountability standards, 3) measure impacts of socially accountable healthcare education, and 4) develop evaluation tools for accreditation activities.Keywords: accreditation research, medical education, 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 | 0.097 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".