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Background <p>Evidence from the global north shows that women and men significantly differ in work accidents and occupational disease rates. However, more data is needed for countries elsewhere.</p> Methods <p>Using natural language processing (NLP), we extracted accident mechanisms from 350,000 admission reports from the largest occupational health provider in Chile. In addition, using the same technique, we normalize occupations written in free text, following the nomenclature from the International Labour Organization (ILO).</p> Results <p>We found that in 57.3% of accidents, a man is affected, while in 42.7% is a woman. The most common occupation for men is operator, while for women, it is related to cleaning duties. The most common form of accident for women is falling from the same height while for men is contact with sharp objects. In this work, we demonstrate the power of NLP in the massive analysis of work-related accidents by reporting the use of large language models with human expert annotation to evaluate mechanisms extraction.</p> Conclusion <p>By sharing our prompts and code, we aim to help other institutions and countries extract crucial information from free text to a controlled vocabulary of ILO. Future work includes the analysis of commuting accidents and occupational diseases.</p>

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Sex differences in work-related accidents extracted from free text in Spanish using natural language processing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月12日

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  • Jocelyn Dunstan,
  • Valentina Campaña-Herrera,
  • Luis Miranda,
  • Rocío Ladrón de Guevara,
  • Pablo Pincheira,
  • Victor Rocco,
  • Daniela Moyano-Dávila

摘要

Background

Evidence from the global north shows that women and men significantly differ in work accidents and occupational disease rates. However, more data is needed for countries elsewhere.

Methods

Using natural language processing (NLP), we extracted accident mechanisms from 350,000 admission reports from the largest occupational health provider in Chile. In addition, using the same technique, we normalize occupations written in free text, following the nomenclature from the International Labour Organization (ILO).

Results

We found that in 57.3% of accidents, a man is affected, while in 42.7% is a woman. The most common occupation for men is operator, while for women, it is related to cleaning duties. The most common form of accident for women is falling from the same height while for men is contact with sharp objects. In this work, we demonstrate the power of NLP in the massive analysis of work-related accidents by reporting the use of large language models with human expert annotation to evaluate mechanisms extraction.

Conclusion

By sharing our prompts and code, we aim to help other institutions and countries extract crucial information from free text to a controlled vocabulary of ILO. Future work includes the analysis of commuting accidents and occupational diseases.