Bibliographic record
Abstract
The present study examines the prevalence of chronic fatigue (CF) among bank workers in Brazil and possible associations with gender and working conditions. The study sample included all 735 workers from the department of data processing of a state bank. CF was assessed using the Chalder Fatigue Scale. Working conditions and socio-demographic, socio-economic and psychosocial factors at work were analysed. Psychiatric symptoms were measured with the SRQ-20. The overall estimate of the prevalence of CF was 8.7% [95% confidence intervals (95% CI) = 6.4-10.9%]: 7.8% (95% CI = 5.5-10.7%) among men and 11.0% (95% CI = 6.7-16.9%) among women. The male-female difference was not statistically significant, even after adjusting for minor psychiatric disorders. The overall prevalence of CF without minor psychiatric disorders was 4.5% (95% CI = 2.7-6.3%): 3.9% (95% CI = 1.9-5.9%) among men and 6.4% (95% CI = 2.0-10.1%) among women. In the final model, risk factors for CF were fast work speed [odds ratio (OR) = 3.5], dissatisfaction at work (OR = 3.1), minor psychiatric disorders (OR = 6.8), and medium (OR = 1.8) and heavy domestic workload (OR = 12.0). CF is common among these bank workers and is associated with psychosocial factors at work. Particularly among women, domestic workload, marital status and the presence of young children were associated with CF in the stratified analysis. Domestic workload may add physical and mental stress, putting employees at risk for CF from overload, or CF may cause workers to perceive domestic work as heavy.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".