Prevalence and Associations of Work Stress and Llfe Stress with Depressive Symptoms among Female Hospital Day Workers and Shift Workers
Bibliographic record
Abstract
Background: Women working in healthcare experience higher levels of work stress compared to the general population. Life stress may also be higher among this population due to how the nature of the job affects workers’ personal lives. Depressive symptoms and depression are also generally higher among female healthcare workers compared to the general population. Objectives: To determine the prevalence of work stress, life stress, and depressive symptoms among female hospital employees and their associations among shift workers and day workers. Methods: A cross-sectional study was conducted at Kingston Health Sciences Centre in Southeastern Ontario between 2011 and 2014 with 168 rotating shift workers and 160 day workers. Levels of work stress were measured using the Job Content Questionnaire and the Siegrist Effort-Reward ratio, life stress was measured using the Derogatis Stress Profile, and depressive symptoms were measured with the Center for Epidemiological Studies Depression Scale. Logistic regression examined associations separately for day and shift workers. Results: Compared to day workers, shift workers experienced higher levels of psychological job demands (p<0.01), physical exertion (p<0.01), and effort spent (p<0.01) on average, and lower levels of overall support at work (p<0.01). Day workers experienced higher job insecurity (p<0.01), over-commitment (p=0.01) and life stress (p=0.03). The prevalence of moderate to severe depressive symptoms was 27% among day workers and 19% among shift workers. Perceived support resulted in reduced prevalence of depressive symptoms (OR 0.74, 95%CI 0.64 to 0.85) among day workers, whereas perceived job insecurity was associated with higher risk of depressive symptoms (OR 1.39, 95%CI 1.06 to 1.83). Over-commitment was associated with higher prevalence of depressive symptoms (day: OR 1.12, 95%CI 1.00 to 1.26; shift: OR 1.18, 95% CI 1.01 to 1.38), whereas higher esteem was associated with lower prevalence of depressive symptoms (day: OR 0.85, 95%CI 0.77 to 0.94; shift: OR 0.87, 95%CI 0.77 to 0.98). Conclusions: Prevalence of depressive symptoms was like that seen in previous studies of female healthcare professionals. Although work status did not meet the statistical criteria as an effect modifier in the association between stress measures and moderate to severe depressive symptoms, some differences were apparent.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".