Assessment of the Relationship between Physical Working Conditions and Different Levels of Work Ability
Bibliographic record
Abstract
Early leaving of workplace by work forces is one of the fundamental problems worldwide. Maintenance and enhancement of employees work ability are important for raising productivity. This study investigated the relationship between work ability index and physical working conditions and was carried out in 2013 on 641 workers at a manufacturing plant in Tehran. Work ability was assessed by the questionnaire of work ability index and the participants were classified into four work ability groups of poor, moderate, good, and excellent. Physical working conditions were evaluated by the MUSIC-Norrtalje questionnaire and the participants were classified into two groups with proper and poor physical working conditions. The mean score of work ability questionnaire was 42.40; and 2.5% (16 persons), 9.2% (59 persons), 38.2% (245 persons), and 50.1% (321 persons) of the participants were in poor, moderate, good, and excellent work ability groups, respectively. The mean score of physical working conditions questionnaire was 20.06. The results of logistic regression analysis showed that even after adjusting the confounding variables, a significant correlation existed between work ability and physical working conditions (p < 0.05). According to the results of this study, there may be a correlation between physical working conditions such as awkward postures, repetitive movements, load lifting, exposure to whole body vibration and so on with work ability. Therefore it seems that enhancement of the quality of physical working conditions may increase work ability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".