Sex differences in work-related accidents extracted from free text in Spanish using natural language processing
Jocelyn Dunstan1,2,3, Valentina Campaña-Herrera4, Luis Miranda4,5
1Department of Computer Science, Pontificia Universidad Católica de Chile, Santiago, Chile. jdunstan@uc.cl.
BMC Public Health
|August 12, 2025
Summary
Men experience more workplace accidents than women, often due to sharp object contact, while women face falls. Natural language processing (NLP) analyzed Chilean occupational health data to reveal these work accident patterns.
Area of Science:
- Occupational Health
- Data Science
- Public Health
Background:
- Global North data indicates significant sex-based differences in work accidents and occupational diseases.
- Limited data exists for occupational health trends in other regions.
Purpose of the Study:
- To analyze sex-based differences in work accident mechanisms in Chile.
- To demonstrate the utility of Natural Language Processing (NLP) for large-scale occupational health data analysis.
Main Methods:
- Utilized NLP to extract accident mechanisms from 350,000 occupational health admission reports in Chile.
- Normalized occupations using free text analysis and the International Labour Organization (ILO) nomenclature.
- Employed large language models with human expert annotation for mechanism extraction validation.
Main Results:
- Men were affected in 57.3% of accidents, women in 42.7%.
- Common male occupations included operators; common female occupations involved cleaning.
- Primary accident mechanisms: men – contact with sharp objects; women – falls from the same height.
Conclusions:
- NLP effectively extracts crucial occupational accident data from unstructured text.
- Shared code and prompts facilitate data extraction for other institutions and countries.
- Future research will address commuting accidents and occupational diseases.
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