Interaction of Physical Exposures and Occupational Factors on Sickness Absence in Automotive Industry Workers
Bibliographic record
Abstract
INTRODUCTION: Increased sickness absence in recent years has been a trouble making issue in industrial society. Identify the causes of sickness absence and its influencing factors, is an important step to control and reduce its associated complications and costs. The aim of this study was to evaluate main factors associated with the incidence of sickness absence. PROCEDURE: In 2012, a cross-sectional study on 758 employees of a car accessories producing company was applied and relevant information about the number of days and episodes of sickness absence, Disease resulting in absence from work, personal features, occupational factors and physical exposures were collected. To determine risk factors associated with sickness absence, Logistic regression analysis was used. RESULTS: The most common diseases leading to sickness absence in order of frequency were Respiratory diseases, musculoskeletal disorders, gastrointestinal diseases and injuries at work. Musculoskeletal disorders increased the danger of long term absence by 4/33 times. Blue collar and shift works were the most important occupational factors associated with the incidence of sickness absence. The main physical factors that affect incidence of sickness absence were frequent bending-twisting and heavy lifting. CONCLUSION: Identifying controllable factors of sickness absence and trying to prevent and modify them such as compliance of ergonomic principals to decrease physical can be effective in reducing sickness absence.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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".