Organizational culture and turnover intention among primary care providers: a multilevel study in four large cities in China
Bibliographic record
Abstract
Background Primary health care plays an important role in providing populations with access to health care. However, it is currently facing unprecedented workforce shortages and high turnover worldwide.Objective This study examined the relationship between organizational culture and turnover intention among primary care providers in China.Methods A cross-sectional survey was administered in four large cities in China, Tianjin, Jinan, Shanghai, and Shenzhen, comprising 38 community health centers and 399 primary care providers. Organizational culture was measured using the Competing Value Framework model, which is divided into four culture types: group, development, hierarchy, and rational culture. Turnover intention was measured using one item assessing participants’ intention to leave their current position in the following year. We compared the turnover intention among different organizational culture types using a Chi-square test, while the hierarchical logistic regression was used to examine the relationship between organizational culture and turnover intention.Results The study found that 32% of primary care providers indicated an intention to leave. Primary care providers working in a hierarchical culture reported higher turnover intention (43.18%) compared with those in other cultures (p < 0.05). Hierarchical culture was a predictor of turnover intention (OR = 3.453, p < 0.001), whereas rational culture had a negative effect on turnover intention (OR = 0.319, p < 0.05).Conclusions Our findings inform organizational management strategies to retain a healthy workforce in primary health care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".