Feedback on feedback: A two-way street between residents and preceptors
Bibliographic record
Abstract
BACKGROUND: Workplace-based assessment (WBA), foundational to competency-based medical education, relies on preceptors providing feedback to residents. Preceptors however get little timely, formative, specific, actionable feedback on the effectiveness of that feedback. Our study aimed to identify useful qualities of feedback for family medicine residents and to inform improving feedback-giving skills for preceptors in PGME training program. METHODS: This study employed a two-phase exploratory design. Phase 1 collected qualitative data from preceptor feedback given to residents through Field Notes (FNs) and quantitative data from residents who provided feedback to preceptor about the quality of the feedback given. Phase 2 employed focus groups to explore ways in which residents are willing to provide preceptors with constructive feedback about the quality of the feedback they receive. Descriptive statistics and a thematic approach were used for data analysis. FINDINGS: We collected 22 FNs identified by residents as being impactful to their learning; analysis of these FNs resulted in five themes. Functionality was then added to the electronic FNs allowing residents to indicate impactful feedback with a "Thumbs Up" icon. Over one year, 895 out of 8,496 FNs (11%) had a "Thumbs up" added, divided into reasons of: confirmation of learning (28.6%), practice improvement (21.2%), new learning (18.8%), motivation (17.7%), and evoking reflection (13.7%). Two focus groups (12 residents, convenience sampling) explored residents' perception of constructive feedback and willingness to also provide constructive feedback to preceptors. CONCLUSION: Adding constructive feedback to existing positive feedback choices will provide preceptors with holistic information about the impact of their feedback on learners, which, in turn, should allow them to provide more effective feedback to learners. However, power differential, relationship impact, and institutional support were concerns for residents that would need to be addressed for this to be optimally operationalized.
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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 | 0.076 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".