Unemployment in an Underserviced Specialty?: The Need for Co-ordinated Workforce Planning in Canadian Neurosurgery
Bibliographic record
Abstract
BACKGROUND: A recent report suggested that newly trained Canadian neurosurgeons are experiencing difficulty finding employment in Canada. Such occurrences, in combination with recent certification restrictions imposed in the US, have resulted in increasing concern that we will shortly be seeing a surplus of graduating neurosurgeons in Canada. The purpose of this study was to develop a better understanding of training and employment patterns in the Canadian neurosurgical workforce. METHODS: Using a database provided by the Royal College of Physicians and Surgeons of Canada, the current practice location of recent (1990-2002) neurosurgical certificants and a list of all neurosurgeons practicing in Canada were generated. From these data the number of surgeons per 100,000 patient population, and the number of residents required to maintain this workforce were determined. RESULTS: Practice location could be identified for 183/189 individuals who passed their qualifying examination in neurosurgery during this time. Only 45% of them are currently practicing in Canada. The current service ratio for this specialty is 0.65 per 100,000 population overall. Although 14.6 residents/year are being trained, only 6.5/year are required to maintain the existing neurosurgical workforce. CONCLUSIONS: Our data supports the concern about an imminent employment crisis for young neurosurgeons in Canada with more than twice the required number of residents being trained. However, this shortfall of staff positions is at a time when the specialty may be underservicing the country's population. These results highlight the necessity for more cohesive workforce planning in Canada, and in particular, ensuring the appropriate balance between training and need.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| 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".