What is the Future of Pediatric Neurology in Canada? Resident and Faculty Perceptions of Training and Workforce Issues
Bibliographic record
Abstract
BACKGROUND: Pediatric neurology trainee numbers have grown considerably in Canada; recent research, however, has shown that the number of pediatric neurology graduates is outpacing the need for future pediatric neurologists. The purpose of this study was to seek the opinion of pediatric neurology program directors and trainees regarding possible solutions for this issue. METHODS: Two focus groups were convened during the Canadian Neurological Sciences Federation annual congress in June 2012; one consisted of current and former program directors, and the other of current pediatric neurology trainees. Groups were asked for their perceptions regarding child neurology manpower issues in Canada as well as possible solutions. Focus groups were audio-recorded and transcribed for analysis. Theme-based qualitative analysis was used to analyze the transcripts. RESULTS: Major themes emerging from both focus groups included the emphasis on community pediatric neurology as a viable option for trainees, including the need for community mentors; recognizing the needs of underserviced areas; and establishing academic positions for community preceptors. The need for career mentoring and support structures during residency training was another major theme which arose. Program directors and trainees also gave examples of ways to reduce the current oversupply of trainees in Canada, including limiting the number of trainees entering programs, as well as creating a long-term vision of child neurology in Canada. CONCLUSIONS: A nationwide dialogue to discuss the supply and demand of manpower in academic and community pediatric neurology is essential. Career guidance options for pediatric neurology trainees across the country merit further strengthening.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".