Career Outcomes Among Neurosurgery Resident Graduates in Canada: An Update
Bibliographic record
Abstract
BACKGROUND: Many Royal College of Physicians and Surgeons of Canada (RCPSC) graduates in neurosurgery face significant challenges in finding full-time employment. The current study describes the career outcomes of neurosurgery residents from Canadian programs. METHODS: = 106) who completed their residency between 2015 and 2020 were included in this study. Baseline characteristics were determined for the entire cohort and then stratified by employment status. Several logistic regression models were used to identify predictors of full-time employment after residency. RESULTS: Overall, 26.4% of neurosurgery graduates from 2015 to 2020 have been underemployed, defined as locum and clinical associate positions (6.6%), the pursuit of multiple fellowships (16%) and career change/nonsurgical career (3.8%). Only 52.0% of graduates were fully employed in Canada, with 30.2% appointed at academic institutions. Skull-base/open vascular (OR: 0.055, 95%CI [<0.01, 0.74]) and general neurosurgery (OR: 0.027, 95% CI [<0.01, 0.61]) fellowships were associated with underemployment. Advanced research degrees (master's or Ph.D.) and sex were not associated with full-time employment. CONCLUSIONS: Over one-quarter of recent Canadian neurosurgery graduates were underemployed, and nearly half do not find employment in Canada. These results reflect a concerning reality for current and prospective neurosurgery graduates in Canada and will hopefully serve as a call to action for the Canadian neurosurgery community.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".