The Canadian general surgery resident: defining current challenges for surgical leadership
Bibliographic record
Abstract
BACKGROUND: Surgery training programs in Canada and the United States have recognized the need to modify current models of training and education. The shifting demographic of surgery trainees, lifestyle issues and an increased trend toward subspecialization are the major influences. To guide these important educational initiatives, a contemporary profile of Canadian general surgery residents and their impressions of training in Canada is required. METHODS: We developed and distributed a questionnaire to residents in each Canadian general surgery training program, and residents responded during dedicated teaching time. RESULTS: In all, 186 surveys were returned for analysis (62% response rate). The average age of Canadian general surgery residents is 30 years, 38% are women, 41% are married, 18% have dependants younger than 18 years and 41% plan to add to or start a family during residency. Most (87%) residents plan to pursue postgraduate education. On completion of training, 74% of residents plan to stay in Canada and 49% want to practice in an academic setting. Almost half (42%) of residents identify a poor balance between work and personal life during residency. Forty-seven percent of respondents have appropriate access to mentorship, whereas 37% describe suitable access to career guidance and 40% identify the availability of appropriate social supports. Just over half (54%) believe the stress level during residency is manageable. CONCLUSION: This survey provides a profile of contemporary Canadian general surgery residents. Important challenges within the residency system are identified. Program directors and chairs of surgery are encouraged to recognize these challenges and intervene where appropriate.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".