Pediatric Neurology Workforce in Canada: A 5-Year Update
Bibliographic record
Abstract
BACKGROUND: In 2013, a task force was developed to discuss the future of the Canadian pediatric neurology workforce. The consensus was that there was no indication to reduce the number of training positions, but that the issue required continued surveillance. The current study provides a 5-year update on Canadian pediatric neurology workforce data. METHODS: Names, practice types, number of weekly outpatient clinics, and dates of certification of all physicians currently practicing pediatric neurology in Canada were obtained. International data were used to compute comparisons between countries. National data sets were used to provide information about the number of residency positions available and the number of Canadian graduates per year. Models for future projections were developed based on published projected population data and trends from the past decade. RESULTS: The number of pediatric neurologists practicing in Canada has increased 165% since 1994. During this period, wait times have not significantly shortened. There are regional discrepancies in access to child neurologists. The Canadian pediatric neurology workforce available to see outpatient consultations is proportionally less than that of USA. After accounting for retirements and emigrations, the number of child neurologists being added to the workforce each year is 4.9. This will result in an expected 10-year increase in Canadian pediatric neurologists from 151 to 200. CONCLUSIONS: Despite an increase in the number of Canadian child neurologists over the last two decades, we do not predict that there will be problems with underemployment over the next decade.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".