The timing of transition to senior surgical resident: a national survey of Canadian program directors
Bibliographic record
Abstract
Introduction: Transitions within medical education are challenging and mark significant changes in responsibility and independence. The transition from junior to senior surgical resident has been sparsely examined. The purpose of this study was to evaluate the timing of this transition in Canadian surgical programs and the factors used to guide this decision. Methods: We developed a cross-sectional, single-stage survey and distributed it to all Canadian surgical program directors. We analyzed survey responses using quantitative methods. Results: Forty-seven program directors responded, representing all ten surgical disciplines. The most frequent period of transition from junior to senior resident was July of PGY-3. Programs that employ a formal "transition" curriculum for juniors had a significantly earlier transition, while programs that use staff feedback to guide the transition decision had a significantly later transition. Program directors identified year of training and experience, technical ability, and clinical competence as key features of a senior surgical resident. Conclusions: Surgical residency programs largely use a time-based model to determine when residents transition from junior to senior resident. Future qualitative studies should examine the factors used to make transition decisions and explore how programs define a senior surgical resident.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".