Continuous quality improvement in an accreditation system for undergraduate medical education: Benefits and challenges
Bibliographic record
Abstract
BACKGROUND: Accreditation reviews of medical schools typically occur at fixed intervals and result in a summative judgment about compliance with predefined process and outcome standards. However, reviews that only occur periodically may not be optimal for ensuring prompt identification of and remediation of problem areas. AIMS: To identify the factors that affect the ability to implement a continuous quality improvement (CQI) process for the interval review of accreditation standards. METHODS: Case examples from the United States, Canada, the Republic of Korea and Taiwan, were collected and analyzed to determine the strengths and challenges of the CQI processes implemented by a national association of medical schools and several medical school accrediting bodies. The CQI process at a single medical school also was reviewed. RESULTS: A functional CQI process should be focused directly on accreditation standards so as to result in the improvement of educational quality and outcomes, be feasible to implement, avoid duplication of effort and have both commitment and resource support from the sponsoring entity and the individual medical schools. CONCLUSIONS: CQI can enhance educational program quality and outcomes, if the process is designed to collect relevant information and the results are used for program improvement.
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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 | 0.127 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".