Pediatrician workforce planning: The Israeli experience and projections of pediatric manpower 1995–2025
Bibliographic record
Abstract
Introduction: In the coming years a worldwide physician shortage is expected. However, little is known regarding pediatrician workforce planning worldwide. The main factors influencing the shortage of physicians include a long and expensive training, physicians’ migration, changing career patterns, early retirement, gender and novel technologies. In this ecological descriptive study we studied past (1995-2006) and future (through 2025) pediatrician workforce trends in Israel. We compared the Israeli pediatrician workforce to other countries. Main outcome: ratio of pediatricians < 65 years-of-age per 1,000 children. Results: The recommended ratio of pediatricians in the USA per 1,000 children grew by more than 50% from 1990 (0.49) to 2000 (0.77). In 2006, the average ratio of pediatricians in Israel per 1,000 children was 10% less than that recommended in the USA. Average ratio of pediatricians per 1,000 children in 77 countries is half (0.38) of the recommended ratio in USA, year 2000. Conclusion: Lack of system outcomes measures, in addition to differences, between countries of about 100%, regarding the required ratios of pediatricians/per 1,000 children provides us with an evidence that one parameter cannot forecast an efficient pediatric workforce. Since planning is a very complex task, decision makers in different health care systems need more indices to plan a cost effective pediatric workforce.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".