Bibliographic record
Abstract
PURPOSE: Role modeling by staff physicians is a significant component of the clinical teaching of students and residents. However, the importance of resident role modeling has only recently emerged, and residents' understanding of themselves as role models has yet to be explored. This study sought to understand residents' perceptions of themselves as role models, describe how residents learn about role modeling, and identify ways to improve resident role modeling. METHOD: Fourteen semistructured interviews were conducted with residents in internal medicine, general surgery, and pediatrics at the McGill University Faculty of Medicine between April and September 2013. Interviews were audio-recorded and subsequently transcribed for analysis; iterative analysis followed principles of qualitative description. RESULTS: Four primary themes were identified through data analysis: residents perceived role modeling as the demonstration of "good" behaviors in the clinical context; residents believed that learning from their role modeling "just happens" as long as learners are "watching"; residents did not equate role modeling with being a role model; and residents learned about role modeling from watching their positive and negative role models. CONCLUSIONS: While residents were aware that students and junior colleagues learned from their modeling, they were often not aware of role modeling as it was occurring; they also believed that learning from role modeling "just happens" and did not always see themselves as role models. Helping residents view effective role modeling as a deliberate process rather than something that "just happens" may improve clinical teaching across the continuum of medical education.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".