通讯作者: 杨卓轶, 18236011@qq.com

DOI：10.12201/bmr.202606.00005

Construction of a Biosafety Competency Evaluation Index System for Emergency Medical Service Nursing Personnel and Comprehensive Evaluation Using PSO-BP Neural Network

• 摘要：目的/意义 应急医学救援队伍护理人员的生物安全胜任能力关系到突发公共卫生事件、重大传染病疫情及生物安全事件中的救援任务完成质量，但目前该领域尚缺乏将智能算法应用于综合评价的研究。本文构建应急医学救援队伍护理人员生物安全胜任能力评价指标体系，采用层次分析法确定指标权重并进行综合评价，进而探索PSO-BP神经网络对AHP综合评价结果的拟合效果，以期为后续构建评价工具提供方法学基础。方法/过程 运用文献分析法、专家访谈法和德尔菲法构建评价指标体系；采用层次分析法确定指标权重并计算综合评价值；以AHP综合评价值作为网络训练的期望输出，建立PSO-BP神经网络模型；从203组数据中随机抽取153组为训练集、25组为验证集、25组为测试集，对模型进行检验。结果/结论 构建的评价指标体系涵盖职业素养与应急准备、基础及急重症护理、生物防御护理、生物安全防疫防护4个维度及13项指标，通过专家一致性检验（权威系数0.91）；PSO-BP神经网络输出值与AHP综合评价值的拟合误差小于2%，决定系数R2接近1，提示模型能够较好拟合AHP综合评价结果。本研究构建的评价指标体系可为该领域综合评价提供一种结构化工具，PSO-BP神经网络模型可较好地拟合AHP综合评价结果，初步验证了该框架对AHP评价结果的拟合能力，为后续引入客观数据、构建评价工具提供了方法学基础

  关键词： 粒子群优化；BP神经网络；应急医学救援护理人员；生物安全；综合评价; ; ; ; 

   

  Abstract: Abstract: Purpose/Significance The biosafety competency of nursing personnel in emergency medical service teams is directly related to the quality of emergency rescue missions in public health emergencies, major infectious disease outbreaks, and biosafety incidents. However, studies on the application of intelligent algorithms to comprehensive evaluation in this field remain limited. To construct a biosafety competency evaluation index system for nursing personnel in emergency medical service teams, and to explore the fitting effect of the PSO-BP neural network on AHP comprehensive evaluation results, providing a methodological foundation for developing evaluation tools in this field. Method/Process An evaluation index system was constructed through literature analysis, expert interviews, and the Delphi method. The Analytic Hierarchy Process (AHP) was used to determine indicator weights and calculate comprehensive evaluation values. A PSO-BP neural network model was established using AHP comprehensive evaluation values as expected outputs for network training. From 203 datasets, 153 were randomly selected as the training set, 25 as the validation set, and 25 as the test set for model validation. Result/Conclusion The constructed evaluation index system, comprising 4 dimensions and 13 indicators, passed expert consistency testing with an expert authority coefficient of 0.91. The fitting error between the PSO-BP neural network output values and the AHP comprehensive evaluation values was less than 2%, and the coefficient of determination R2 approached 1, indicating that the model could fit AHP comprehensive evaluation results well. The evaluation index system constructed in this study provides a structured tool for comprehensive evaluation in this field. Building on this foundation, the PSO-BP neural network model demonstrated good fitting performance against AHP comprehensive evaluation results, providing a methodological foundation for extending the evaluation model with objective data and developing evaluation tools in the future.

  Key words: particle swarm optimization; BP neural network; emergency medical service nursing personnel; biosafety; comprehensive evaluation

  提交时间：2026-06-02

  版权声明：作者本人独立拥有该论文的版权，预印本系统仅拥有论文的永久保存权利。任何人未经允许不得重复使用。
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• 序号	提交日期	编号	操作
  1	2025-09-16	10.12201/bmr.202606.00005V1	下载

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