Resident Behaviours to Prioritize According to Canadian Plastic Surgeons
Bibliographic record
Abstract
INTRODUCTION: Many articles have been published outlining the resident selection process for plastic surgery training programs. However, which qualities Canadian plastic surgeons value most in their current residents remains unclear. A national survey study was conducted to identify which attributes surgeons associate with the highest resident performance and which behaviours trainees should prioritize during their training. METHODS: A literature review was performed to identify studies that documented attributes valued in plastic surgery applicants and characteristics of high-performing surgical residents. These qualities were extracted to construct a survey consisting of both ranking and open-ended questions. After an iterative review process, the survey was disseminated nationally to consultants and trainees of Canadian plastic surgery training programs. RESULTS: Survey responses were obtained from 120 invitees and a weighted rank was calculated for each evaluated attribute. The terms integrity, professional, and work ethic were viewed as the most important attributes prized by surgeons. Dishonesty, lack of dependability, and unprofessionalism were viewed as the most concerning behaviours. Additionally, disinterest and arrogance were identified by the open-ended questions as behaviours surgeons would like to see less frequently in their trainees. When compared to surgeons, trainees undervalued the importance of knowledge and the impact of unprofessional behaviour. CONCLUSIONS: With the multiple roles that a resident must fulfill, understanding which attributes are of the most importance will help focus self-directed learning and development within residency programs. Ultimately, instilling the importance of integrity and professionalism is most highly valued by members of the Canadian plastic surgery community.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".