Primary care workforce and continuous medical education in China: lessons to learn from a nationwide cross-sectional survey
Bibliographic record
Abstract
OBJECTIVES: This study aimed to examine the education and training background of Chinese community health centres (CHCs) staff, continuous medical education (CME) and factors affecting participation in CME. DESIGN: Cross-sectional survey. SETTING: CHCs). PARTICIPANTS: All doctors and nurses working in selected CHCs (excluding those solely practising traditional Chinese Medicine). MAIN OUTCOME MEASURES: CME recorded by CHCs and self-reported CME participation. METHODS: A stratified random sample of CHCs based on geographical distribution and 2:1 urban-suburban ratio was selected covering three major regions of China. Two questionnaires, one for lead clinicians and another for frontline health professionals, were administered between September-December 2015, covering the demographics of clinic staff, staff training and CME activities. RESULTS: 149 lead clinicians (response rate 79%) and 1734 doctors and 1846 nurses completed the survey (response rate 86%). Of the doctors, 54.5% had a bachelor degree and only 47% were registered as general practitioners (GPs). Among the doctors, 10.5% carried senior titles. Few nurses (4.6%) had training in primary care. Those who have reported participating in CME were 91.6% doctors and 89.2% nurses. CME participation in doctors was more commonly reported by older doctors, females, those who were registered as a GP and those with intermediate or senior job titles. CME participation in nurses was more common among those with a bachelor degree or intermediate/senior job titles or those with longer working experience in the CHC. CONCLUSION: Only half of doctors have bachelor degrees or are registered as GPs as their prime registration in the primary care workforce in China. The vast majority of CHC staff participated in CME but there is room for improvement in how CME is organised.
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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 | 0.003 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".