Mental disorders in judicial workers: analysis of sickness absence in a cohort study
Bibliographic record
Abstract
OBJECTIVE: To analyze risk factors for sickness absence due to mental disorders among judicial workers in Bahia, Brazil. METHODS: Retrospective cohort with follow-up from 2011 to 2016 with 2,660 workers of a judicial sector in Bahia, Brazil. The main outcome measures were survival curves estimated for the independent variables using the Kaplan-Meier product limit estimator and risk factors for the first episode of sickness absence calculated based on the Cox regression model. RESULTS: The survival estimate of the population of this study for the event was 0.90 and from the Cox model the risk factors for the first episode of sickness absence due to mental disorders were: female (HR = 1.81), occupation of magistrate (HR = 1.80), and age over 30 years old (HR = 1.84). In addition, the risk for new cases of sickness absence among women reached 4.0 times the risk for men, in 2015. The estimated relative risks of sickness absence and the observed survival reduction behavior over time add information to the literature on sociodemographic and occupational factors associated with sickness absence due to mental disorders in the public sector. CONCLUSION: These results highlight the need for further research to more precisely identify vulnerable groups at risk of preventable mental health-related sickness absence in the workplace, better identify the workplace organizational factors that contribute to these disorders as well as studies on the effectiveness of workplace interventions to improve mental health among judicial and other public sectors workers.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".