The Pediatrician Workforce in the United States and China
Bibliographic record
Abstract
ABSTRACT: From 2019 to 2022, the For Our Children project gathered a team of Chinese and American pediatricians to explore the readiness of the pediatric workforce in each country to address pressing child health concerns. The teams compared existing data on child health outcomes, the pediatric workforce, and education and combined qualitative and quantitative comparisons centered on themes of effective health care delivery outlined in the World Health Organization Workforce 2030 Report. This article describes key findings about pediatric workload, career satisfaction, and systems to assure competency. We discuss pediatrician accessibility, including geographic distribution, practice locations, trends in pediatric hospitalizations, and payment mechanisms. Pediatric roles differed in the context of each country's child health systems and varied teams. We identified strengths we could learn from one another, such as the US Medical Home Model with continuity of care and robust numbers of skilled clinicians working alongside pediatricians, as well as China's Maternal Child Health system with broad community accessibility and health workers who provide preventive care.In both countries, notable inequities in child health outcomes, evolving epidemiology, and increasing complexity of care require new approaches to the pediatric workforce and education. Although child health systems in the United States and China have significant differences, in both countries, a way forward is to develop a more inclusive and broad view of the child health team to provide truly integrated care that reaches every child. Training competencies must evolve with changing epidemiology as well as changing health system structures and pediatrician roles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".