Diagnosis About Work Accidents in Textile Industry: Insights to Implement Occupational Health and Safety Systems
Bibliographic record
Abstract
Purpose: This study aimed to analyze the occupational health and safety data in the textile industry to guide the implementation of the ISO 45001 standard. Theoretical Framework: The global estimates of the International Labor Organization show that the world economy loses about 4% of GDP annually to occupational diseases and accidents, which, in addition to human losses, result in a loss of productivity due to unsafe or unhealthy environments. Motivated by the transition from the OHSAS 18000 standard to the ISO 45001, it is necessary to understand the scenario of industries and the impact that accidents cause. Design/Methodology/Approach: From the collection of data from the state of Santa Catarina in the southern region of Brazil, a diagnosis is presented that may serve as a starting point for improvement actions regarding worker health and safety and as a benchmark for other companies in other sectors. The methodology began with analyzing the state of the art in occupational health and safety management, accident concepts, and the history of this theme worldwide. Findings: The results showed that the main accidents that occurred in the textile factories of Santa Catarina from 2012 to 2022 were with machinery and equipment, followed by accidents with chemical agents, transport vehicles, and biological agents. As for the most affected body parts, these were the fingers, followed by feet, hands (except wrists and fingers), and eyes. Another research question was to identify the sectors of the textile industry that had the most accidents in the period, which were the spinning, weaving, and textile processing sectors. Also, there were two thousand days lost in 2021 alone and, cumulatively, 45,900 days lost in this interim. Research, Practical & Social Implications: The absence of studies of this type for the textile industry and also a starting point for improvement actions regarding worker health and safety and as a benchmark for other companies in other sectors. The adoption of an Occupational Health and Safety Management System with the application of the ISO 45001 standard is a preventive and necessary measure to reduce the rates of accidents and diseases raised in the research carried out. Originality/Value: The relevance of the topic is demonstrated by the large number of accidents registered in Brazil and worldwide and, at the same time, the absence of studies of this type for the textile industry. The clipping of this sector helps to understand the data regarding the most affected body parts, the number of registered work accidents, expenses and waste for companies, their causes, and the sector in which the most accidents occur, thus guiding managers to the implementation of an effective occupational health and safety management system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".