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使用分类与回归树分析预测临床护士因患者安全事件导致的工作与生活平衡:一项二次数据分析

Clinical nurses' work-life balance prediction due to patient safety incidents using classification and regression tree analysis: a secondary data analysis.

作者信息

Kang Jiwon, Kwon Soon-Sun, Lee Youngjin

机构信息

Department of Family, Health and Wellbeing, University of Minnesota Extension, 1420 Eckles Ave, St Paul, MN, 55108, US.

College of Nursing, Ajou University, 164 World Cup-Ro, Yeongtong-Gu, Suwon, 16499, South Korea.

出版信息

BMC Nurs. 2024 Jan 25;23(1):70. doi: 10.1186/s12912-024-01719-0.

DOI:10.1186/s12912-024-01719-0
PMID:38267902
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10809596/
Abstract

BACKGROUND

Patient safety incidents lead to performance difficulties for nurses when providing nursing practice. This affects work-life balance and causes second and third-victimization. This study predicts factors affecting clinical nurses' work-life balance due to patient safety incidents using classification and regression tree analysis techniques.

METHODS

This study was a secondary analysis of data from a cohort research project, which used a descriptive survey for data collection. Participants comprised 372 nurses. Data were collected using SurveyMonkey, a mobile-based survey software solution, from January to September 2021. Data included the general characteristics of clinical nurses, second damage, second damage support, third damage, and work-life balance. The specific variables included in the analysis chosen through rigorous Lasso analysis form the foundation for predicting work-life balance. Variables with low explanatory power were excluded, thereafter, the variables selected by Lasso were analyzed with a classification and regression tree model to predict work-life balance.

RESULTS

A regression tree was applied to predict work-life balance using seven variables-education level, marital status, position, physical distress, second-victim support, turnover intentions, and absenteeism (selected through Lasso analysis). After pruning, at tree size four, when turnover intentions were < 4.250, physical distress < 2.875, and second-victim support < 2.345, the predicted work-life balance was 3.972. However, when turnover intentions were < 4.250, physical distress < 2.875, and second-victim support ≥ 2.345, then the predicted work-life balance was 2.760.

CONCLUSIONS

This study's insights offer crucial groundwork for crafting targeted workforce risk management strategies and fostering a conducive organizational culture to mitigate nursing occupational stress, potentially curbing the recurrence of patient safety incidents and improving nursing practice while enhancing patient outcomes. Future research should explore second and third victim experiences across various healthcare settings globally to understand their impact on WLB and patient safety outcomes.

摘要

背景

患者安全事件会给护士在提供护理服务时带来工作上的困难。这会影响工作与生活的平衡,并导致二次和三次受害。本研究使用分类和回归树分析技术预测因患者安全事件影响临床护士工作与生活平衡的因素。

方法

本研究是对一项队列研究项目数据的二次分析,该项目使用描述性调查收集数据。参与者包括372名护士。2021年1月至9月期间,使用基于移动设备的调查软件解决方案SurveyMonkey收集数据。数据包括临床护士的一般特征、二次伤害、二次伤害支持、三次伤害以及工作与生活平衡。通过严格的套索分析选择纳入分析的具体变量,为预测工作与生活平衡奠定基础。排除解释力低的变量,之后,用分类和回归树模型分析套索选择的变量,以预测工作与生活平衡。

结果

应用回归树,使用七个变量——教育水平、婚姻状况、职位、身体困扰、二次受害支持、离职意向和旷工(通过套索分析选择)来预测工作与生活平衡。修剪后,在树大小为4时,当离职意向<4.250、身体困扰<2.875且二次受害支持<2.345时,预测的工作与生活平衡为3.972。然而,当离职意向<4.250、身体困扰<2.875且二次受害支持≥2.345时,预测的工作与生活平衡为2.760。

结论

本研究的见解为制定有针对性的劳动力风险管理策略和培育有利的组织文化以减轻护理职业压力提供了关键基础,这可能会抑制患者安全事件的再次发生,改善护理实践并提高患者治疗效果。未来研究应探索全球不同医疗环境中的二次和三次受害经历,以了解它们对工作与生活平衡及患者安全结果的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4fd/10809596/44dfaa9d2685/12912_2024_1719_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4fd/10809596/6b8975ff1051/12912_2024_1719_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4fd/10809596/44dfaa9d2685/12912_2024_1719_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4fd/10809596/6b8975ff1051/12912_2024_1719_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4fd/10809596/44dfaa9d2685/12912_2024_1719_Fig2_HTML.jpg

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