Does the Implementation of Competency-Based Medical Education Impact the Quality of Narrative Feedback? A Retrospective Analysis of Assessment Data in a Canadian Internal Medicine Residency Program
Bibliographic record
Abstract
Background: As training programs implement competency-based models of training oriented around entrustable professional activities (EPAs), the role of traditional assessment tools remains unclear. While rating scales remain emphasized, few empirical studies have explored the utility of narrative comments between methods and models of training. Objective: Compare the quality of narrative comments between in-training evaluation reports (ITERs) and workplace-based assessments (WBAs) of EPAs before and after the formal implementation of a competency-based model of training. Methods: Retrospective analysis of assessment data from 77 residents in the core Internal Medicine (IM) residency program at the University of Calgary between 2015 and 2020, including data collected during a 2-year pilot of WBAs before the official launch of Competency by Design on July 1, 2019. The quality of narrative comments from 2,928 EPAs and 3,608 ITERs was analyzed using the standardized Completed Clinical Evaluation Report Rating (CCERR). Results: CCERR scores were higher on EPAs than ITERs [F (26,213) = 210, MSE = 4,541, p < 0.001, η2 = 0.064]. CCERR scores for EPAs decreased slightly upon formal implementation of Competence by Design but remained higher than the CCERR scores for ITERs completed at that period of time. Conclusions: The quality of narrative comments may be higher on EPAs than traditional ITER evaluations. While programmatic assessment requires the use of multiple tools and methods, programs must consider whether such methods lead to complementarity or redundancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".