| 摘要(英) |
In recent years, various industries in Taiwan have been facing severe labor shortages, with the construction industry being particularly affected by a lack of workers. To address the labor shortage, the government relaxed regulations on the recruitment of foreign migrant workers in June 2023, leading to a significant increase in their numbers. However, with the influx of foreign migrant workers, the number of serious occupational accidents may also increase substantially, raising widespread social attention towards the workplace safety of foreign migrant workers.
This study aims to systematically analyze data on major occupational accidents to observe the differences in the causes of accidents between foreign migrant workers and local workers, and propose countermeasures. The research data includes a total of 1,857 cases, mainly in the construction industry, of which 58 cases involved foreign migrant workers and 1,799 cases involved local workers. First, the local worker data was sampled (317 cases) and combined with the foreign migrant worker data (58 cases) for chi-square testing to screen the categories. Descriptive statistics were used to analyze categories such as accident types and causal attributes, initially revealing differences in the causes of occupational accidents between the two groups.
Next, the FCA technique was employed to uncover hidden association rules from the complex cases, and the rules were visualized using concept lattices and credibility lists. The research will conduct LCA to understand the latent distribution patterns of foreign migrant workers and local workers in engineering projects based on the attribute combinations, with the results cross-checked using chi-square tests and validated with logistic regression. Subsequently, the association rules from FCA will be extracted.
Finally, through the application of both FCA and LCA analysis tools, along with logistic regression validation, when dealing with construction projects, construction sites, and potentially hazardous work environments, the probability table of occupational accident categories generated by LCA and the odds ratio equations for each attribute derived from logistic regression can be used, as these two results contribute significantly to safety assessment. High-risk areas such as excavation sites or heavy machinery operation areas require extra caution and enhanced preventive measures to avoid serious occupational accidents. These research findings can serve as an important basis for future formulation of occupational accident risk assessments, thereby ensuring the safety of workers in their workplaces. |
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