Characteristics and Workload of Pediatricians in China
Bibliographic record
Abstract
OBJECTIVES: Although it is widely believed that China is facing a major shortage of pediatricians, the real situation of the current national status of pediatric human resources and their working conditions has not been evaluated to date. METHODS: We administered a survey to 54 214 hospitals from all 31 provinces in mainland China from 2015 to 2016. Hospital directors of all secondary and tertiary hospitals with pediatric services and a random sample (10%) of primary hospitals provided information on number of pediatricians and their educational levels, specialties, workloads, dropout rates, and other hospital characteristics. A data set of medical resources and socioeconomic information regarding each region (1997-2016) was constructed from the Chinese National Statistics Bureau. The Gini coefficient was used to describe the geographical distributions of pediatricians and hospitals. RESULTS: There were 135 524 pediatricians in China or ∼4 pediatricians per 10 000 children. Pediatricians' average educational level was low, with ∼32% having only 3 years of junior college training after high school. The distribution of pediatricians was extremely skewed (Gini coefficient 0.61), and the imbalance of highly educated pediatricians was even more skewed (Gini coefficient 0.68). The dropout rate of pediatricians was 12.6%. Despite an increase in the Chinese government's financial investment in health over the last decade, physicians have been burdened with a greater workload. CONCLUSIONS: Uneven development of the pediatric care system, inadequately trained pediatricians, low job satisfaction, and unmet demand for pediatric care are the major challenges facing China's pediatric health care system.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".