Mental health service use and its associated factors among nurses in China: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: To facilitate mental health service planning for nurses, data on the patterns of mental health service use (MHSU) among nurses are needed. However, MHSU among Chinese nurses has seldom been studied. Our study aimed to explore the rate of MHSU among Chinese nurses and to identify the factors associated with MHSU. METHODS: A self-designed anonymous questionnaire was used in this study. MHSU was assessed by the question, "Have you ever used any kind of mental health services, such as mental health outpatient services or psychotherapies, when you felt that your health was suffering due to stress, insomnia, or other reasons?" The answer to the question was binary (yes or no). Sleep quality, burnout, and depressive symptoms were assessed using the Chinese version of the Pittsburgh Sleep Quality Index , the Chinese version of the Maslach Burnout Inventory-General Survey and the two-item Patient Health Questionnaire, respectively. Chi-square tests and binary logistic regression were used for univariate and multivariate analyses. RESULTS: A total of 10.94% (301/2750) of the nurses reported MHSU. 10.25% (282/2750) of the nurses had poor sleep quality, burnout and depressive symptoms, and only 26.95% of these nurses reported MHSU. Very poor sleep quality (OR 9.36, 95% CI [5.38-16.29]), mid-level professional title (OR 1.48, 95% CI [1.13-1.93]) and depressive symptoms (OR 1.66, 95% CI [1.28-2.13]) were independent factors associated with MHSU. CONCLUSIONS: Most of the nurses have experienced burnout, poor sleep quality or depressive symptoms and the MHSU rate among them was low. Interventions to improve the mental health of nurses and to promote the use of mental health services are needed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".