Assessing the quality of feedback to general internal medicine residents in a competency-based environment.
Bibliographic record
Abstract
CONSTRUCT: Competency Based Medical Education (CBME) is designed to use workplace-based assessment (WBA) tools to provide observed assessment and feedback on resident competence. Moreover, WBAs are expected to provide evidence beyond that of more traditional mid- or end-of-rotation assessments [e.g., In Training Evaluation Reports (ITERs)]. In this study, we investigated the quality of feedback in General Internal Medicine (GIM), by comparing WBA and ITER assessment tools. BACKGROUND: WBAs are hypothesized to improve written and numerical feedback to support the development and documentation of competence. In this study, we investigated residents' and preceptors' perceptions of WBA validity, usability, and reliability and the extent to which WBAs differentiate residents' performance when compared to ITERs. APPROACH: We used a mixed methods approach over a three-year period, including perspectives gathered from focus groups, interviews, along with numerical and narrative comparisons between WBA and ITERs in one GIM program. RESULTS: Our quantitative analysis of feedback from seven residents' clinical assessments showed that overall rates of actionable feedback, for both ITERs and WBAs, were low (26%), with only 9% of the total providing an improvement strategy. The provision of quality feedback was not significantly different between tools; although WBAs provided more actionable feedback, ITERs provided more strategies. We found that residents and preceptors indicated the narrative component of feedback was more constructive and effective than numerical scores. Both groups perceived the focus on specific workplace-based feedback was more effective than ITERs. CONCLUSIONS: Participants in this study viewed narrative, actionable, and specific feedback as essential, and an overall preference was found for written feedback over numerical assessments. However, our quantitative analyses showed that specific actionable feedback was rarely documented, despite finding an emphasis from both residents and preceptors of its importance for developing competency. Neither formative WBAs nor summative ITERs clearly provided better feedback, and both may still have a role in overall resident evaluation. Participant views differed in roles and responsibilities, with residents stating that preceptors should be responsible for initiating assessments and vice-versa. These results reveal an incongruence between resident and preceptor perceptions and practice around giving feedback and emphasize opportunities for programs adopting and implementing CBME to address how best to support residents and frontline clinical teachers.
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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 | 0.030 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".