Relationship between occupational stress and occupational injury of workers in a cable manufacturing enterprise by decision tree model
- VernacularTitle:某电缆制造企业工人职业紧张与职业伤害的关系探讨—基于决策树模型
- Author:
Ting XU
1
,
2
;
Juan QIAN
3
;
Yishuo GU
4
;
Daozheng DING
3
;
Jianjian QIAO
3
;
Yong QIAN
3
;
Xiaojun ZHU
4
;
Jingguang FAN
4
Author Information
- Publication Type:Specialcolumn:Occupationalinjuriesandtheirinfluencingfactors
- Keywords: occupational stress; occupational injury; epidemiological characteristics; decision tree; cable worker
- From: Journal of Environmental and Occupational Medicine 2025;42(2):145-150
- CountryChina
- Language:Chinese
-
Abstract:
Background Social psychological factors have emerged as a key area of research in occupational injury prevention. Occupational stress, a significant component of social psychology, has garnered widespread attention due to its potential impact on occupational injury. Objective To analyze the factors influencing occupational stress among cable manufacturing workers and explore the relationship between occupational stress and occupational injury, and to provide scientific evidence for reducing occupational stress and injury. Methods A questionnaire on basic demographics, occupational injury, and occupational stress (Effort-Reward Imbalance, ERI) was used to investigate
1343 workers in a cable enterprise. Chi-square test was employed to analyze the distribution of occupational stress across different worker characteristics. Logistic regression was used to identify factors influencing occupational stress, and a decision tree model was applied to assess the impact of occupational stress on injury risk. Results A total of1225 valid questionnaires were collected, yielding a response rate of 91.2%. The positive rate of occupational stress was 28.7%, while the incidence of occupational injury in the past year was 8.6%. The results of univariate analysis revealed that workers who had experienced occupational injuries had a higher positive rate of occupational stress compared to those without injuries (P<0.05), and there were significant differences in the distribution of occupational stress by genders, overtime work, shift work, daily working hours, fatigue levels, sleep duration, family relationships, and colleague relationships (P<0.05). The logistic regression results showed that individuals who worked shifts, had fair/poor/very colleague relationships, and frequently worked overtime had a higher risk of occupational stress (P<0.05). The final decision tree model confirmed that occupational stress was a risk factor for occupational injury. Additional factors influencing occupational injury included physical exercise, colleague relationships, pre-job training participation, and gender. Workers who exercised less than twice per week and experienced occupational stress had a significantly higher risk of injury (23.6%) compared to those without occupational stress (10.9%). Conclusion Shift work, colleague relationships, and overtime work are correlated with the positive rate of occupational stress among cable manufacturing workers. Implementing a reasonable work schedule and fostering a positive work environment are recommended. Reducing occupational stress may help lower the risk of occupational injuries in this industry.
