Turnover intention among primary health workers in China: a systematic review and meta-analysis
Bibliographic record
Abstract
Objectives To analyse the prevalence and determinants of turnover intention (TI) among primary health workers (PHWs) in China to provide evidence for improving retention measures. Design Systemic review and meta-analysis. Data sources Four English-language databases (PubMed, EMBASE, Cochrane Library, PsycINFO) and three Chinese databases (CNKI, CSPD, CBM) were searched up to October 2019. Eligibility criteria Eligible studies were observational or descriptive studies conducted in mainland China. The prevalence of TI among health workers and related factors had to be explicitly reported in each included study. Data extraction and synthesis Data were extracted by one author and reviewed independently by two other authors. For each factor analysed by a meta-analysis, the factor was required to be the same across different studies, and at least three studies had to include it. The quality of studies was assessed using the Newcastle–Ottawa Scale and heterogeneity was evaluated using the I 2 statistic. Results We identified 16 cross-sectional studies investigating a total of 37 672 PHWs. The prevalence of TI was 30.4%. Subgroup analysis revealed that the highest prevalence was observed in the community primary healthcare institutions and the eastern provinces of China. Meta-analyses indicated that 21 factors were significantly associated with TI, including demographic factors (gender, age, education, marital status), job characteristic factors (title, work seniority, remuneration, social status, organisational affiliation, work stress) and job satisfaction factors (learning and training opportunity, interpersonal relationship, work condition and environment, and so on). Conclusion This study highlights the problem of TI among PHWs in China. Efforts should be made to improve conditions in both work-related areas and areas outside of work. Policymakers should continue to improve reward systems, the construction of infrastructure and promotion systems, and pay more attention to PHWs’ lives outside of work and meet their living needs.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.028 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".