Understanding the emotional effects of competency-based education
Bibliographic record
Abstract
Introduction: The Royal College of Physicians and Surgeons of Canada has shifted to a competency-based medical education (CBME) model that employs an outcomes-based learning approach for resident training with a focus on skills development, rather than a time-based model. The goal of CBME is to improve resident feedback and enhance medical education quality. However, research suggests CBME may not improve feedback and that residents may experience higher levels of stress, anxiety, and exhaustion. Although research exists regarding CBME’s theoretical benefits, little is known about its emotional impacts. This study aims to identify and understand the emotional effects of CBME on residents, faculty, and administrators in Psychiatry.\nMethods: This study employs a qualitative methodology. Approximately six participants are being recruited per group (i.e., residents, faculty, and administrators) from McMaster University’s Psychiatry department. Participants are undergoing semi-structured, one-on-one interviews where they are being asked open-ended questions that probe their emotions and experiences with CBME. Interviews are being transcribed and analyzed using a line-by-line approach that generates individual meaning units.\nResults: To date, data have been collected for 4 residents and 4 faculty members. Interim analysis suggests mainly negative or neutral emotions related to CBME, including feelings of frustration and tiredness.\nConclusions: This study is helping to elucidate the emotional effects of CBME on residents, faculty, and administrators in Psychiatry. Findings from this study will contribute to the growing scientific literature on CBME’s subjective effects and inform local quality improvement efforts.\nThis study was approved by the Hamilton Integrated Research Ethics Board.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".