通过考虑合成人口来确定职业事故基线比率:西班牙的案例
Jordi Olivella Nadal1,2, Gema Calleja Sanz1,2, Ignacio Fuentes Ribas3
1Institute of Industrial and Control Engineering, and Management Department, Universitat Politècnica de Catalunya, Barcelona, Spain.
PloS one
|November 22, 2023
概括
这项研究引入了一种用于比较不同地区和部门的职业事故数据的新方法. 它创建了一个标准化的基线,使得事故趋势和风险因素的分析更可靠.
科学领域:
- 职业健康和安全问题 职业健康和安全问题
- 数据科学数据科学数据科学
- 统计建模 统计建模
背景情况:
- 国际上对职业事故进行比较往往是矛盾的,因为数据收集方法和因素各不相同.
- 现有的数据缺乏统一的基线,用于跨不同特征进行一致的比较分析.
研究的目的:
- 开发一种标准化的方法来比较职业事故数据.
- 创建一个单一的基准来分析各种人口和活动部门的事故率.
主要方法:
- 选择关键因素:年龄,性别,自治社区和活动.
- 使用最佳代表性样本权重 (rsw) 生成合成人口.
- 使用XGBoost决策树组合预测事故率.
主要成果:
- 提出的方法成功地产生了代表西班牙语背景的合成人口.
- XGBoost模型有效地预测了定义特征集的事故率.
- 结果证实了该方法适用于创建统一基线的适当性.
结论:
- 开发的方法为标准化的职业事故数据比较提供了一个强大的框架.
- 这种方法可以适应在其他国家和环境中使用.
- 它促进了对职业安全和风险的更准确,更可靠的见解.
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