Occupational stress in industry setting in Benin 2019: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Occupational stress is a psychosocial risk in the workplace. Working conditions in industrial settings may lead to occupational stress. In Benin, however, there is little epidemiological data on occupational stress in industrial settings. We aimed to determine the prevalence and factors associated with occupational stress in industrial settings in Benin in 2019. METHODS: This was a prospective, cross-sectional study conducted from January 31 to April 11, 2019, among 15 cotton ginning plants. Sampling was exhaustive for permanent workers and stratified in clusters by shift for occasional cotton gin workers. Data were collected through Karasek and Siegrist questionnaires. Data analysis was performed using R software. Binary multivariable logistic regression was performed. The significance level was p < 0.05. RESULTS: Of 1883 workers included, 90.8% were male. The median age was 38 years (IQR: 28 years to 49 years). The prevalence of occupational stress was 77.7% (95% CI: 75.8-79.6). Psychological demand was high in 93.0% of workers and 83.9% had low decision latitude. Among the workers, 16.3% had low social support and 89.9% had a low recognition score at work. Factors associated with occupational stress were: being an occasional vs. permanent worker (aOR 6.43, 95% CI 4.18 to 9.88); age less than 38 years (aOR 0.55, 95% CI 0.41 to 0.76); high intensity physical activity at work (aOR 1.33, 95% CI 1.03 to 1.73); working in production vs. administration (aOR 1.59, 95% CI 1.03 to 2.45); spending fewer than 4 years at the current work location (aOR 1.60, 95% CI 1.05 to 2.44); and scoring low for recognition at work (aOR 1.53, 95% CI 1.04 to 2.23). Noise exposure and being a shift worker were significant in univariable analysis, but not multivariable analysis. CONCLUSION: Occupational stress is very common among workers in industrial settings. The implementation and evaluation of preventive measures against these risk factors is necessary.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".