Canadian Paediatric Neurology Workforce survey and consensus statement
Bibliographic record
Abstract
Background: Little knowledge exists on the availability of paediatric neurology positions. Our study aimed to: 1) obtain information regarding the availability of positions for paediatric neurologists in academic centres; 2) survey paediatric neurology trainees regarding their perceptions of employment issues and career plans; 3) survey practicing community paediatric neurologists 4) convene a group of paediatric neurologists to develop a consensus regarding how to address these workforce issues. Methods: Surveys addressing workforce issues regarding paediatric neurology in Canada were sent to: 1) all paediatric neurology program directors in Canada who then solicited information from division heads and from paediatric neurologists in surrounding areas; 2) paediatric neurology trainees in Canada and; 3) community paediatric neurologists. A meeting was held with relevant stakeholders to develop a consensus on how to approach employment issues. Results: We found that the number of projected positions in academic paediatric neurology is fewer than the number of paediatric neurologists that are being trained. Paediatric neurology residents are concerned about job availability and desire more career counselling. Conclusions: There is a current and projected clinical demand for paediatric neurologists despite a lack of academic positions. Training programs should focus on community neurology as a viable career option.
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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 | 0.037 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".