Associations between psychosocial factors and work ability in a Tunisian electricity and gas company
Bibliographic record
Abstract
Introduction Work ability can be influenced by numerous factors, particularly psychosocial ones. These latter can be individual psychosocial factors but also psychosocial factors at the workplace. Objectives This study aimed to explore psychosocial determinants of work ability among workers in a Tunisian electricity and gas company. Methods We conducted a cross-sectional survey among 83 male workers in a Tunisian electricity and gas company. We used a self-administered questionnaire that included socio-demographic profile, psychosocial factors assessment through the Job content questionnaire (JCQ) and General Health Questionnaire (GHQ-12), and Work Ability Index (WAI) questionnaire. Data were analysed using SPSS software. We used the student’s test to compare means between two groups. Results The mean WAI score among workers in the studied electricity and gas company was 8.96 (SD=1.37). At the time of the survey, one person out of 3 among the participants suffered from a psychological distress (37.3% with a GHQ-12 score ≥ 3). These Workers had a weaker work ability compared to those with not (p=0.033). We found also that having low social support and passive jobs were associated with low work ability (p=0.003 and p=0.005 respectively). Conclusions Most personal and occupational psychosocial factors had significant associations with WAI in the studied company. Thus, enhancing the psychosocial environment in the workplace can promote work ability in such occupations. Disclosure of Interest None Declared
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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) | 0.003 | 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".