Meta-analysis of nursing-related organizational and psychosocial predictors of sickness absence
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Nursing is a stressful occupation with high rates of sickness absence. To date, there are no meta-analyses that statistically determined the correlates of sickness absence in this population. AIMS: This meta-analysis examined organizational and psychosocial predictors of sickness absence among nursing staff. METHODS: As a registered systematic review (PROSPERO: CRD42017071040), which followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, five databases (CINAHL, PROQuest Allied, PROQuest database theses, PsycINFO, PubMed) were reviewed to examine predictors of sickness absence in nurses and nursing assistants between 1990 and 2019. The Population/Intervention/Comparison/Outcome tool was used to support our searches. Effect sizes were analysed using random-effects model. RESULTS: Following critical appraisals using (i) National Institutes of Health's Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies and (ii) Strengthening the Reporting of Observational Studies in Epidemiology, 21 studies were included. Nursing assistants had greater odds of sickness absence than nurses. Working night shifts, in paediatrics or psychiatric units, experiencing poor mental health, and fatigue, also increased the odds of sickness absence. There was no evidence that job satisfaction or job strain influenced sickness absence; however, job demand increased the likelihood. Finally, work support reduced the odds of lost-time. CONCLUSIONS: We synthesized three decades of research where several factors influenced sickness absence. Due to limited recent research, the results should be interpreted with caution as some practices may have changed overtime or between countries. Nevertheless, these findings could help in applying preventative strategies to mitigate lost-time in a vulnerable working population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it