Attitudes of Canadian medical students towards surgical training and perceived barriers to surgical careers: a multicentre survey
Bibliographic record
Abstract
Background: Medical student interest in surgical specialties continues to decline. This study aims to characterize attitudes of Canadian medical students towards surgical training and perceived barriers to surgical careers. Methods: An anonymous survey was custom designed and distributed to medical students at the University of Alberta and University of Calgary. Survey questions characterized student interest in surgical specialties, barriers to pursuing surgery, and influence of surgical education opportunities on career interest. Results: Survey engagement was 26.7% in 2015 and 24.2% in 2021. General surgery had the highest rate of interest in both survey years (2015: 38.3%, 2021: 39.2%). The most frequently reported barrier was worry about the stress that surgical careers can put on personal relationships (2015: 70.9%, 2021: 73.8%, p = 0.50). Female respondents were significantly more likely to cite gender discrimination as a deterrent to surgical careers (F: 52.0%, M: 5.8%, p < 0.001). Conclusions: Despite substantial interest, perception of work-life imbalance was the primary reported barrier to surgical careers. Further, female medical students’ awareness of gender discrimination in surgery highlights the need for continued efforts to promote gender inclusivity within surgical disciplines to support early career women interested in surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".