Depressive Symptoms and Associated Factors among Employees in Public Utility Places of Zhejiang Province, China
Bibliographic record
Abstract
Depression has been known to adversely influence the working performance of employees. However, compared with physical health, few studies have reported the prevalence of depressive symptoms among employees in workplaces. This study aimed to investigate the current situation and the relationships among the prevalence of depressive symptoms, physical health, social–psychological factors, working conditions, environmental perception, and workplace performance of employees in public utility places in Zhejiang Province, China. A cross-sectional study was conducted during the fourth quarter of 2019. A total of 1232 individuals responded to the survey. Six types of information—sociodemographic characteristics, working conditions, psychosocial factors, four groups of physical symptoms, working performance, and depressive symptoms—were collected. Logistic regression analysis was performed. The survey results showed that the prevalence of depressive symptoms among employees in public utility places in Zhejiang Province was 17.9%. Perceptions of a poor work environment, poor interpersonal relationships, an unfulfilling life, and frequent ocular and general uncomfortable symptoms were associated with a higher risk of depressive symptoms. Strong work ability, fulfilment in life, and taking the initiative to wear or use protective equipment at work were associated with a lower risk of depressive symptoms. The results of this study will provide valuable guidance for the scientific intervention for depressive symptoms among public utility place employees in the future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".