Content and Rationale of Junior and Senior Preceptors Responding to Residents' Educational Needs Revisited
Bibliographic record
Abstract
THEORY: Feedback and debriefing, as portrayed in expertise development and self-assessment, play critical roles in providing residents with useful information to foster their progress. HYPOTHESES: Prior work has shown that clinical preceptors' use of conceptual frameworks (CFs; ways of thinking based on theories, best practices, or models) while giving feedback to residents was positively associated with a greater diversity of responses. Also, senior preceptors produced more responses, used more CFs, and asked more probing-challenging questions than junior preceptors. The purpose was to confirm the generalization of these initial findings with a broader and better defined sample of preceptors. METHOD: We conducted a mixed-method study with 20 junior and 20 senior preceptors in a controlled environment to analyze their responses and rationales to residents' educational needs as portrayed in 6 written vignettes. The preceptors were recruited from 3 primary care specialties preceptors (family medicine, internal medicine, pediatrics) of the 3 French-speaking faculties of medicine in Québec, Canada. RESULTS: The preceptors increased the 2012 list of response topics (96 to 126) and doubled the number of distinct CFs (16 to 32). The junior and senior preceptors expressed the same number and diversity of CFs. On average, senior preceptors asked more clarification questions and reflected more than juniors on the learning process that occurs during case discussions. Preceptor specialty and prior training in medical education did not influence the number and diversity of responses and CFs, except that preceptors with prior training generated more responses per vignette and were more reflective. Senior preceptors had a stronger positive relationship between the number of total and distinct CFs and the number of responses than the juniors. CONCLUSIONS: Although senior preceptors did not give more responses or use more CFs compared to the prior study, they continue to probe residents more and reflected more. The positive relationship between responses and CFs has important implications for faculty development and calls for more research to better understand the specific contribution of CFs to feedback.
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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 | 0.035 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".