Content and Conceptual Frameworks of Preceptor Feedback Related to Residents’ Educational Needs
Bibliographic record
Abstract
PURPOSE: The development of clinical expertise depends not only on frequent practice opportunities but also on receiving quality feedback, especially regarding difficult aspects of learning. The purpose of this study was to investigate the content and conceptual frameworks of preceptor feedback to residents during case presentations. METHOD: The authors conducted a qualitative and correlational study in which 25 clinical preceptors from one Canadian medical school's internal medicine and family medicine residency programs responded to six written, case-based vignettes depicting residents seeking help regarding a variety of educational issues. Preceptors were asked probing follow-up questions about their responses. The authors analyzed response content, conceptual frameworks used in formulating responses, and the correlation between the two. RESULTS: Overall, the preceptors generated 806 responses, representing 96 distinct topics. The five topics mentioned most frequently related to reading suggestions, leading diagnosis, contrasting clinical findings, patient follow-up, and resident's concerns/feelings about the case. Seventy-three percent of the topics were specific to one or two vignettes. The preceptors used 18 distinct conceptual frameworks in formulating responses (e.g., analytical versus nonanalytical reasoning, problem representation, therapeutic alliance, patient-centered approach). Use of conceptual frameworks was positively associated with greater diversity of responses (r = 0.43, P = .03). CONCLUSIONS: The vignettes stimulated rich and extensive lists of topics and conceptual frameworks. These findings represent but one step in the exploration of the content and conceptual frameworks of preceptor feedback and of the interrelatedness of feedback content and process, which have important implications for teaching and faculty development.
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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 | 0.026 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".