The Role of Senior Resident Clinics in Plastic Surgery Education in Canada
Bibliographic record
Abstract
BACKGROUND: Senior resident clinics are a means to encourage independent practice and problem solving and enhance surgical skills. The objective of this study is to investigate senior resident clinics across Canada and their utility in providing comprehensive plastic surgery training. METHODS: A web-based survey was sent to all plastic surgery program directors (PDs) and senior residents (SRs; postgraduate years 3, 4, and 5) across Canada. The surveys focused on demographics, clinic structure, procedures commonly performed, perceived autonomy, educational benefit, competency-based design considerations, and areas for improvement. Chi-square tests were used to compare responses between PDs and SRs. RESULTS: A total of 10 PDs (100% response rate) and 26 SRs (41% response rate) responded. Half of the training programs across Canada currently have senior clinics, and the format varies between institutions. Clinics generally focus on hand trauma and aesthetics. Both PDs and SRs felt that there is considerable autonomy for resident care in both the pre/post-operative and operative setting. Common barriers to implementing a senior clinic include not enough staff, not enough time, and the medicolegal risk. Most core competencies are felt to be addressed through the use of senior clinics. Methods to improve senior clinics could include more regular and higher volume clinics, enhanced equipment, and separation of hand and aesthetics clinics. CONCLUSIONS: Senior clinics are a useful method to improve plastic surgery education and address many core aspects of plastic surgery training. Implementation of supported clinics focused on hand and aesthetics surgery separately may be useful for training programs that currently lack a senior clinic.
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".