Paediatrician human resource planning in Canada: A 10-year follow-up
Bibliographic record
Abstract
BACKGROUND: Paediatrician human resource planning in Canada is currently a major concern. The optimal mix of physicians by type of practice and geographical distribution also remains controversial for many groups of physicians. OBJECTIVE: To compare 10-year trends (1987 to 1997) in paediatric practice with respect to age, sex and percentage of paediatricians practicing tertiary care. METHODS: Information on the demographics and practice patterns of Canadian paediatricians obtained from national surveys conducted in 1987 and 1997 was examined. RESULTS: In the 1987 survey, 1960 paediatricians were mailed a questionnaire, and 1352 questionnaires were returned (response rate of 69%). In the 1997 survey, 1706 of 2337 paediatricians returned the completed questionnaire (response rate of 73%). In 1987, 26.2% of paediatricians were women compared with 38.5% in 1997 (P<0.0001). When men and women were combined, 14.5% of paediatricians were in the 25- to 34-year age bracket in 1987, compared with only 9.7% in 1997 (P=0.0002). In 1987, 37.7% of paediatricians reported practicing tertiary care versus 38.7% in 1997 (P=0.61). In addition, tertiary care paediatricians have become more centralized in communities with more than 100,000 people. CONCLUSIONS: The results confirm that the paediatric workforce is aging, located primarily in large urban areas and shifting toward more women. Shortages of paediatricians, especially in remote and rural areas, continue to be a major concern and show no signs of improvement. The potential impact of these changes on delivery and quality of child-care services in the future needs to be assessed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".