Accreditation of Canadian Undergraduate Medical Education Programs: A Study of Measures of Effectiveness
Bibliographic record
Abstract
PURPOSE: Undergraduate medical education (UME) programs participate in accreditation with the belief that it contributes to improving UME quality and, ultimately, patient care. Linkages between accreditation and UME quality are incomplete. Previous studies focused on student performance on national examinations, medical school processes, medical school's organizational culture types, and degree of implementation of quality improvement activities as markers of the effectiveness of accreditation. The current study sought to identify new indicators of accreditation effectiveness, to better understand the value and impact of accreditation. METHOD: This qualitative study used an expert-oriented evaluation approach to identify novel markers of accreditation effectiveness. From March 2015 to March 2016, leaders and teachers at 16 of the 17 Canadian UME programs were invited to participate in interviews and focus group discussions aimed at identifying measures of accreditation effectiveness. Themes were extracted using the method of constant comparative analysis. RESULTS: Sixty-three individuals from 13 (81%) medical schools participated. Eight themes were formulated: Student/graduate performance, UME program processes, quality assurance and continuous quality improvement, stakeholder satisfaction, stakeholder expectations, engagement, research, and UME program quality. The latter 5 themes have not been previously studied as measures of accreditation effectiveness. All themes appear applicable to accreditation of graduate medical education as well. A framework is proposed to guide future research on the impact of accreditation. CONCLUSIONS: Eight themes were generated, representing direct and indirect indicators of the impact of accreditation. The themes are integrated into a framework proposed to guide future research on the value of accreditation along the continuum of medical education.
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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 | 0.065 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".