Hidden Curriculum Effects in the Canadian PGME Accreditation System as Experienced by Program Directors and Program Administrators
Bibliographic record
Abstract
Introduction: Canadian residency accreditation requires that training programs evaluate the influence of the hidden curriculum as part of their continuous improvement process, but the hidden curriculum influences exerted by the accreditation system on training programs have yet to be explored. This study explores hidden curriculum effects of accreditation structures, processes, and cultures as experienced by residency program directors and program administrators. Methods: Semi-structured interviews with five leads from the Canadian Residency Accreditation Consortium (CanRAC) were conducted to explore the purposes of accreditation and how structures, processes, and cultures are designed to support those purposes. Semi-structured interviews with eight program directors, and eight program administrators from residency training programs across Canada were also conducted to explore how their experiences with accreditation were in alignment or misalignment with the intended purposes. Transcripts were coded and then organized by themes. The hidden curriculum effects were identified from these themes. Results: Six themes were identified from an analysis of the codes: Purposes of Accreditation, Continuous Improvement, Accreditation Standards, Whose Voice Matters, What Accreditation Tells Us about Programs, and Communication Gatekeepers and the “Blackbox.” Within these themes, six purposes of accreditation and 16 hidden curriculum effects were further identified. Conclusion: Program directors and program administrators experience hidden curriculum effects resulting from the structures, processes, and cultures of the accreditation system. The quality assurance aspect of accreditation looms over program directors and program administrators, making it difficult for them to fully experience the continuous improvement aspect of the accreditation process. Building stronger communication pathways that encourage the sharing of information between programs and the accrediting colleges, and encourage collaboration between review teams and program leaders, may help counteract many of the negative hidden curriculum effects and increase the utility of accreditation reports. Future research should investigate ways to strengthen positive and reduce negative hidden curriculum influences in PGME accreditation. This research has the potential for application in other program evaluation models, as the approach of exploring with administrative staff the hidden curriculum effects created by the broader accreditation systems could be employed in other contexts or disciplines.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".