Making assessment a team sport: a qualitative study of facilitated group feedback in internal medicine residency
Bibliographic record
Abstract
Purpose: Competency-based medical education relies on feedback from workplace-based assessment (WBA) to direct learning. Unfortunately, WBAs often lack rich narrative feedback and show bias towards Medical Expert aspects of care. Building on research examining interactive assessment approaches, the Queen's University Internal Medicine residency program introduced a facilitated, team-based assessment initiative ("Feedback Fridays") in July 2017, aimed at improving holistic assessment of resident performance on the inpatient medicine teaching units. In this study, we aim to explore how Feedback Fridays contributed to formative assessment of Internal Medicine residents within our current model of competency-based training. Method: A total of 53 residents participated in facilitated, biweekly group assessment sessions during the 2017 and 2018 academic year. Each session was a 30-minute facilitated assessment discussion done with one inpatient team, which included medical students, residents, and their supervising attending. Feedback from the discussion was collected, summarized, and documented in narrative form in electronic WBA forms by the program's assessment officer for the residents. For research purposes, verbatim transcripts of feedback sessions were analyzed thematically. Results: The researchers identified four major themes for feedback: communication, intra- and inter-personal awareness, leadership and teamwork, and learning opportunities. Although feedback related to a broad range of activities, it showed strong emphasis on competencies within the intrinsic CanMEDS roles. Additionally, a clear formative focus in the feedback was another important finding. Conclusions: The introduction of facilitated team-based assessment in the Queen's Internal Medicine program filled an important gap in WBA by providing learners with detailed feedback across all CanMEDS roles and by providing constructive recommendations for identified areas for improvement.
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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 | 0.042 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".