Recent Trends in Neurosurgery Career Outcomes in Canada
Bibliographic record
Abstract
BACKGROUND: As with other specialties, Royal College of Physicians and Surgeons of Canada (RCPSC) trainees in Neurosurgery have anecdotally had challenges securing full-time employment. This study presents the employment status, research pursuits, and fellowship choices of neurosurgery trainees in Canadian programs. METHODS: RCPSC neurosurgery trainees (n = 143) who began their residency training between 1998 and 2008 were included in this study. Associations between year of residency completion, research pursuits, and fellowship choice with career outcomes were determined by Fisher's exact test (p < 0.05, statistical significance). RESULTS: In 2015, 60% and 26% of neurosurgery trainees had permanent positions in Canada and the USA, respectively. Underemployment, defined as locum and clinical associate positions, pursuit of multiple unrelated fellowships, unemployment, and career change to non-surgical career, was 12% in 2015. The proportion of neurosurgery trainees who had been underemployed at some point within 5 years since residency completion was 20%. Pursuit of in-folded research (MSc, PhD, or non-degree research greater than 1 year) was significantly associated with obtaining full employment (94% vs. 73%, p = 0.011). However, fellowship training was not significantly associated with obtaining full employment (78% vs. 75%, p = 1.000). CONCLUSIONS: Underemployment in neurosurgery has become a significant issue in Canada for various reasons. Pursuit of in-folded research, but not fellowship training, was associated with obtaining full employment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".