Inconsistent role modeling of professionalism in family medicine residency: Resident perspectives from 2 Ontario sites.
Bibliographic record
Abstract
OBJECTIVE: To explore how family medicine (FM) residents experience role modeling of professionalism by FM preceptors. DESIGN: Qualitative design using semistructured, one-on-one interviews. SETTING: Two FM teaching units at the University of Toronto in Ontario. PARTICIPANTS: Sixteen first- and second-year FM residents. METHODS: This study employed a qualitative description design. The CanMEDS-Family Medicine 2009 framework was used to help design interview questions. Interviews were audiorecorded and transcribed verbatim. Transcripts were coded and themes were developed. MAIN FINDINGS: Some residents described insufficient experience with role modeling in general. Two main findings were that a longitudinal relationship with a role model was important and that residents desired a close working relationship with a role model in a clinical setting. Most participants could identify experiences with role modeling of ethical practice; many examples were in the context of challenging patients. Some, but not all, residents could identify experiences with role modeling of profession-led regulation and reflective practice. Of note, there were mixed responses with respect to role modeling a commitment to personal health. CONCLUSION: Reassuringly, many FM residents described experiences with positive role modeling of professionalism. However, some residents believed that role modeling was limited by the brevity of their interactions with potential role models. To optimize the effect of role modeling, educators should support opportunities for residents to develop close, longitudinal working relationships with faculty.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".