Adapting Feedback to Individual Residents: An Examination of Preceptor Challenges and Approaches
Bibliographic record
Abstract
ABSTRACT Background Feedback conversations between preceptors and residents usually occur in closed settings. Little is known about how preceptors address the challenges posed by residents with different skill sets, performance levels, and personal contexts. Objective This study explored the challenges that preceptors experienced and approaches taken in adapting feedback conversations to individual residents. Methods In 2015, 18 preceptors participated in feedback simulations portraying residents with variations in skill, insight, confidence, and distress, followed by debriefing of the feedback conversation with a facilitator. These interactions were recorded, transcribed, and analyzed using thematic and framework analysis. Results The preceptors encountered common challenges with feedback conversations, including uncertainty in how to individualize feedback to residents and how to navigate tensions between resident- and preceptor-identified goals. Preceptors questioned their ability to enhance skills for highly performing residents, whether they could be directive when residents had insight gaps, how they could reframe the perceptions of the overly confident resident, and whether they should offer support to emotionally distressed residents or provide feedback about performance. Preceptors adapted their approach to feedback, drawing on techniques of coaching for highly performing residents, directing for residents with insight gaps, mediation with overly confident residents, and mentoring with emotionally distressed residents. Conclusions Examining the feedback challenges preceptors encounter and the approaches taken to adapt feedback to individual residents can provide insight into how preceptors meet the challenges of competency-based medical education, in which frequent, focused feedback is essential for residents to achieve educational milestones and entrustable professional activity expectations.
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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 | 0.043 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".