Fostering trust, collaboration, and a culture of continuous quality improvement: A call for transparency in medical school accreditation
Bibliographic record
Abstract
Medical schools provide the foundation for a physician's growth and lifelong learning. They also require a large share of government resources. As such, they should seek opportunities to maintain trust from the public, their students, faculty, universities, regulatory colleges, and each other. The accreditation of medical schools attempts to assure stakeholders that the educational process conforms to appropriate standards and thus can be trusted. However, accreditation processes are poorly understood and the basis for accrediting authorities' decisions are often opaque. We propose that increasing transparency in accreditation could enhance trust in the institutions that produce society's physicians. While public reporting of accreditation results has been established in other jurisdictions, such as Australia and the United Kingdom, North American accrediting bodies have not yet embraced this more transparent approach. Public reporting can enhance public trust and engagement, hold medical schools accountable for continuous quality improvement, and can catalyze a culture of collaboration within the broader medical education ecosystem. Inviting patients and the public to peer into one of the most formative and fundamental parts of their physicians' professional training is a powerful tool for stakeholder and public engagement that the North American medical education community at large has yet to use.
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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 | 0.182 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.071 |
| Scholarly communication | 0.039 | 0.037 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.018 | 0.033 |
| 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".