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利用机器学习预测南非环境下护士的适应能力。

Using Machine Learning to Predict Resilience Among Nurses in a South African Setting.

作者信息

Chipps Jennifer, Cromhout Amanda, Tokac Umit

机构信息

School of Nursing, Faculty of Community Health Sciences, University of Western Cape, Cape Town 7441, South Africa.

College of Nursing, University of Missouri, St. Louis, MO 63121, USA.

出版信息

Int J Environ Res Public Health. 2025 Jun 24;22(7):996. doi: 10.3390/ijerph22070996.

Abstract

Nursing is a stressful profession. Stress can affect the mental health of nurses. A positive response to stress, resilience, is known to be a protective factor against mental health issues. This study aimed to use machine learning with secondary data from five survey studies, conducted between 2022 and 2023, to identify factors predicting high versus low levels of resilience in South African nursing samples from the Western Cape Province, South Africa. The sample included (1134 records (male = 250, 22.0%, female = 874, 77.1%, and other = 10 (0.9%) included all data on all categories of nursing staff (student nurses (567, 50%), professional registered nurses (315, 27.8%), and non-professional nurses (246, 21.7%) who completed a survey using a response to stress scale. We used random forest analysis, demographic variables, years of experience, and a brief 4-item screen of resilience to predict resilience. The model yielded limited added value from demographic groupings in this model, but the brief screening had an overall classification accuracy of 86.41% (95% CI: 0.810; 0.908).

摘要

护理是一个压力较大的职业。压力会影响护士的心理健康。对压力的积极反应,即心理韧性,被认为是预防心理健康问题的一个保护因素。本研究旨在利用机器学习和2022年至2023年期间进行的五项调查研究的二手数据,以确定预测南非西开普省护理样本中高心理韧性水平与低心理韧性水平的因素。样本包括1134条记录(男性 = 250人,占22.0%;女性 = 874人,占77.1%;其他 = 10人,占0.9%),涵盖了所有类别护理人员(学生护士(567人,占50%)、专业注册护士(315人,占27.8%)和非专业护士(246人,占21.7%))使用压力反应量表完成调查的所有数据。我们使用随机森林分析、人口统计学变量、工作年限以及一个简短的4项心理韧性筛查量表来预测心理韧性。该模型在人口统计学分组方面产生的附加值有限,但简短筛查的总体分类准确率为86.41%(95%置信区间:0.810;0.908)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b721/12294786/7182439d942d/ijerph-22-00996-g001.jpg

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