Relationship between Quality of Life and Occupational Accidents in South-East of Iran (Zahedan)
Bibliographic record
Abstract
With the development of science and technology, occupational accidents, as one of the most important problems in the world, result in negative effects on physical and psychological health, and also the quality of life of workers. The aim of this study was to compare the quality of life among workers with and without accident. In a cross-sectional study, 93 workers were selected, 31 who experienced accident and 62 as control group. To gather the data, a researcher-made questionnaire for demographic characteristics and the quality of life questionnaire (SF-36) were used. Mann-Whitney and Chi-square tests were used for data analysis. The mean and standard deviation of age was 30.81±7.29 and 30.56±7.19 in workers with accident (case group) and control group, respectively. Homogeneity was ensured in terms of age and work experience and the two groups had no significant difference in this regard (p>0.05). Most of the participants were high school graduates (67.7%). The majority of accidents (68.8%) had occurred in the manufacturing sections. The most common accident type was sprayed chemical substances (19.4%) and the less frequent was electrocution (3.2%). The mean total score for the quality of life was 37.61±14.29 and 74.92±12.95 in the case and control groups with a statistical significance difference (p<0.001). The results of this study indicate that the incident could affect the quality of life of workers. Therefore, promoting the safety culture can help to reduce the occupational accidents.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".