Chronicling the Transition to Competency-Based Medical Education in a Small Subspeciality Program
Bibliographic record
Abstract
Background As medical education programs transition to competency-based medical education (CBME), experiences transitioning in the context of small subspecialty programs remain unknown, yet they are needed for effective implementation and continual improvements. Objective To examine faculty and resident experiences transitioning to CBME in a small subspeciality program. Methods Using a qualitative descriptive approach and constructivist lens, faculty and residents in McMaster University’s geriatric psychiatry subspecialty program were interviewed about their transition experiences between November 2021 and February 2022, after the program’s soft launch of CBME in 2020. Interviews were transcribed and data were analyzed using thematic analysis. Reflexive memo writing and investigator and data triangulation strategies were employed to ensure rigor and trustworthiness of the data. Results Ten of the 17 faculty members (59%) and 3 residents (100%) participated. Six themes were developed: (1) Both faculty and residents see themselves as somewhat knowledgeable about CBME, but sources of knowledge vary; (2) More frequent feedback is beneficial; (3) Aspects of CBME that are challenging for residents are beneficial for faculty; (4) Competence committees are perceived positively despite most participants’ limited firsthand experience with them; (5) Small program size is both a barrier and facilitator to providing and receiving feedback; and (6) Suggestions for improvement are centered on helping manage faculty and resident workload imposed by CBME. Conclusions Incongruent expectations surrounding entrustable professional activity management were highlighted as an area requiring support. Collegial relationships among faculty and residents made it difficult for faculty to provide constructive feedback but improved residents’ perceptions of the feedback.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".