P.169 Canadian neurosurgical attrition rate: a qualitative exploration
Bibliographic record
Abstract
Background: Canadian neurosurgery residency programs have an alarming 28.4% attrition rate—seven times higher than the average for most other specialties (1–4%) and double that of US neurosurgery programs. Canadian data for this issue is over 30 years old, highlighting the need for updated research. This study identifies factors contributing to Canadian neurosurgery attrition rates. Methods: Using critical constructivist theory, virtual interviews were conducted with current program directors (PDs) from Canada’s 14 neurosurgery programs and neurosurgery residents who left training between 2013–2023. Interviews were recorded, transcribed, anonymized, and iteratively coded through descriptive thematic analysis to construct an analytical framework. Results: We conducted interviews with 7 PDs and 7 former neurosurgery residents, representing 7 neurosurgery programs across Canada. The average attrition rate was 14.11% (0%–28.6%) from 2013–2023. Contributing factors include poor job prospects in Canada, resource constraints leading to high workloads, poor work-life balance, moral distress due to high levels of patient mortality, and a lack of teaching and support from staff and senior residents. Conclusions: Neurosurgery residents are the future of neurosurgery. Our study uncovers factors contributing to high attrition rates in neurosurgery training, indicating that change must come from provincial governments and within training programs to retain residents.
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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 | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.029 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".