Report of the Canadian Neurological Society Manpower Survey 2012
Bibliographic record
Abstract
BACKGROUND: The Canadian Neurological Society commissioned a manpower survey in 2012 to assess Canadian neurological manpower and resources. METHODS: Surveys were sent electronically to all Canadian neurologists with available email addresses. Responses were analysed for effects of physician gender, age, geographic location (eastern or western Canada) or type of practice (academic, community). Questions focused on work patterns, neurologic conditions treated, access to or performance of procedures, and service and manpower issues. RESULTS: A total of 694 of 854 neurologists in Canada were surveyed and 219 (32%) responded. Respondents were 70% male with mean age of 50 years. Neurologists worked an average of 57 hours/week and saw a mean of 40 patients per week. There were significant differences in number of patients seen, types of practice, and areas of neurological specialization between community and academic neurologists. Fifty percent of neurologists report shortages of neurologists in their community, particularly of general adult neurologists. Wait times for neurological services exceeded international standards for consultations and also were longer than Canadian averages for other specialists. More community (18%) than academic (5%) neurologists planned to retire within the next 5 years. CONCLUSIONS: The demand for neurological services continues to outstrip resources despite the increased number of neurologists. Impending retirement of community neurologists will exacerbate manpower issues unless adequate numbers of trainees choose general neurologic practice in the community as a career.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".