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Article(id=1322169035110863773, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322168583854080893, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.08.007, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1755100800000, receivedDateStr=2025-08-14, revisedDate=1775145600000, revisedDateStr=2026-04-03, acceptedDate=null, acceptedDateStr=null, onlineDate=1793159262682, onlineDateStr=2026-10-28, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793159262682, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793159262682, creator=13701087609, updateTime=1793159262682, updator=13701087609, issue=Issue{id=1322168583854080893, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='8', pageStart='1241', pageEnd='1424', issueExtLink='null', onlineDate='null', pubDate='1777046400000', pubDateStr='2026-04-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793159155095, creator='13701087609', updateTime=1793159155095, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1289, endPage=1297, ext={EN=ArticleExt(id=1322169035303801758, articleId=1322169035110863773, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Construction of the risk prediction model for pediatric nurses' effort-reward imbalance based on interpretable machine learning, columnId=1220689484526572183, journalTitle=Chinese Nursing Research, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=
Objective

To construct the risk prediction model for pediatric nurses' effort-reward imbalance based on interpretable machine learning,and to compare the predictive performance of different models.To explain the results of the optimal model using SHAP interpretation.

Methods

Using the convenience sampling method,a total of 414 pediatric nurses from 6 hospitals in Anhui province,Shanxi province,Jiangxi province and Hunan province in June 2025 were selected as the research subjects.They were randomly divided into training set and validation set at a ratio of 7∶3.The Chinese Nurse Stressor Scale and the Effort-Reward Imbalance Questionnaire scale were used for investigation.LASSO regression was employed to screen the characteristic variables and identify the important predictors.The important predictors were incorporated into the machine learning model to construct three risk prediction models for pediatric nurses' effort-reward imbalance:Logistic regression model,Extreme Gradient Boosting model,and Random Forest model.The areas under the receiver operating characteristic curves(AUC),accuracy,sensitivity,and F1 score of the models were compared to evaluate the predictive performance of the models and select the optimal model.The SHAP explanation was used to interpret the optimal model.

Results

LASSO regression identified three important factors:the number of night shifts per month,workload and time allocation,and educational background.The AUC values of the three prediction models(Logistic regression model,Extreme Gradient Boosting model,and Random Forest model) were 0.725,0.890,and 0.903 respectively,with accuracies of 0.673,0.794,and 0.801,sensitivities of 0.421,0.731,and 0.813,and F1 scores of 0.547,0.773,and 0.798.The SHAP explanation results showed that the importance ranking of the influencing factors were the number of night shifts per month,workload and time allocation,and educational background.

Conclusions

The risk prediction model for pediatric nurses' effort-reward imbalance based on interpretable machine learning constructed by Random Forest has better performance than Logistic regression and Extreme Gradient Boosting model.Personalized predictions should be made based on the number of night shifts each nurse takes per month,workload and time allocation,and educational backgroun.It provides a reference for the early identification of the imbalance between effort and reward for the nurses and the formulation of personalized intervention measures.

, authors=Zhengju CHEN, Xiumei ZHANG, Peng SHAO, authorsList=Zhengju CHEN, Xiumei ZHANG, Peng SHAO, authorCompany=null, correspAuthors=Peng SHAO, authorNote=null, correspAuthorsNote=
SHAO Peng， E-mail：
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322169038793462703, articleId=1322169035110863773, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=基于可解释性机器学习的儿科护士付出-回报失衡风险预测模型的构建, columnId=1220689484681761433, journalTitle=护理研究, columnName=科研论著, runingTitle=null, highlight=null, articleAbstract=
目的

基于可解释性机器学习构建儿科护士付出-回报失衡的风险预测模型，比较不同模型的预测性能，并采用SHAP对最优模型结果进行解释。

方法

采用便利抽样法，于2025年6月选取安徽省、山西省、江西省、湖南省6所医院儿科护士414人作为研究对象，按照7∶3随机分为训练集和验证集。采用护士压力量表、付出-回报失衡量表进行调查。采用LASSO回归对特征变量进行筛选，得出重要预测因子。将重要预测因子纳入机器学习中，构建儿科护士付出-回报失衡的Logistic回归模型、极端梯度提升模型、随机森林模型3种风险预测模型，比较模型的受试者工作特征曲线下面积（AUC）、准确度、灵敏度、F1分数，评价模型的预测性能，筛选最优模型，采用SHAP对最优模型进行解释。

结果

LASSO回归筛选出每个月夜班数、工作量与时间分配、学历3个重要因子。Logistic回归模型、极端梯度提升模型、随机森林模型3种预测模型的AUC分别是0.725，0.890，0.903，准确度为0.673，0.794，0.801，灵敏度为0.421，0.731，0.813，F1分数为0.547，0.773，0.798。SHAP结果显示影响因素重要性排序为每个月夜班数、护士工作量与时间分配、学历。

结论

通过随机森林构建的儿科护士付出-回报失衡风险预测模型性能优于Logistic回归、极端梯度提升模型。应根据护士每个月夜班数、工作量与时间分配、学历进行个性化预测，为其付出-回报失衡的早期识别、制定个性化干预措施提供参考。

, authors=陈正菊, 张秀梅, 邵鹏*, *, authorsList=陈正菊, 张秀梅, 邵鹏, authorCompany=null, correspAuthors=邵鹏, authorNote=

陈正菊，主管护师，本科

, correspAuthorsNote=
邵鹏，E-mail：
SHAO Peng， E-mail：
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language=EN, label=Table 1, caption=

Univariate analysis of factors affecting the effort-reward imbalance of pediatric nurses

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类付出-回报失衡组(n=213)非付出-回报失衡组(n=201)统计值P
年龄（岁）34.51±6.1933.60±5.17t=1.6190.106
是否来自三级医院（人）否3327χ2=0.3540.552
是180174
科室（人）儿内科141138χ2=3.8880.566
儿外科1218
儿科门急诊33
儿科重症监护室（PICU）1512
新生儿重症监护室（NICU）3321
儿科康复病房99
性别（人）男030.226①
女213198
学历（人）专科及以下918χ2=13.4760.001
本科186180
硕士研究生及以上183
职称（人）护士/护师6078χ2=9.0970.011
主管护师141120
副主任护师及以上123
职务（人）科护士长36χ2=7.5450.110
病区护士长186
带教老师2418
专科护士1818
无150153
婚姻状况（人）已婚165168χ2=2.4590.117
未婚4833
子女数量（人）无6051χ2=1.1520.562
1个7278
≥2个8172
每周工作时间（人）≤40 h6990χ2=8.8050.012
41~48 h12387
≥49 h2124
月均收入（人）<5 000元2730χ2=9.7220.021
5 000~10 000元117132
>10 000~15 000元6036
>15 000元93
健康状况（人）差93χ2=15.4310.004
较差309
一般135153
较好2424
好1512
工作-家庭冲突（人）经常2433χ2=2.6910.260
有时147126
几乎没有4242
儿科工作时间（年）10.72±6.6010.58±5.82t=0.2220.824
每个月夜班数（个）7.15±8.006.52±3.65t=1.0250.306
CNSS总分（分）84.45±20.2471.31±26.71t=5.660<0.001
护理专业知识及实践（分）17.11±4.4715.18±5.78t=3.818<0.001
工作量与时间分配（分）13.41±3.4510.67±4.19t=7.268<0.001
工作环境及自愿（分）6.59±2.275.75±2.57t=3.549<0.001
病人护理（分）27.63±7.2023.03±8.99t=5.767<0.001
管理及人际关系（分）19.70±6.4416.69±7.40t=4.435<0.001
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儿科护士付出-回报失衡影响因素的单因素分析

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项目分类付出-回报失衡组(n=213)非付出-回报失衡组(n=201)统计值P
年龄（岁）34.51±6.1933.60±5.17t=1.6190.106
是否来自三级医院（人）否3327χ2=0.3540.552
是180174
科室（人）儿内科141138χ2=3.8880.566
儿外科1218
儿科门急诊33
儿科重症监护室（PICU）1512
新生儿重症监护室（NICU）3321
儿科康复病房99
性别（人）男030.226①
女213198
学历（人）专科及以下918χ2=13.4760.001
本科186180
硕士研究生及以上183
职称（人）护士/护师6078χ2=9.0970.011
主管护师141120
副主任护师及以上123
职务（人）科护士长36χ2=7.5450.110
病区护士长186
带教老师2418
专科护士1818
无150153
婚姻状况（人）已婚165168χ2=2.4590.117
未婚4833
子女数量（人）无6051χ2=1.1520.562
1个7278
≥2个8172
每周工作时间（人）≤40 h6990χ2=8.8050.012
41~48 h12387
≥49 h2124
月均收入（人）<5 000元2730χ2=9.7220.021
5 000~10 000元117132
>10 000~15 000元6036
>15 000元93
健康状况（人）差93χ2=15.4310.004
较差309
一般135153
较好2424
好1512
工作-家庭冲突（人）经常2433χ2=2.6910.260
有时147126
几乎没有4242
儿科工作时间（年）10.72±6.6010.58±5.82t=0.2220.824
每个月夜班数（个）7.15±8.006.52±3.65t=1.0250.306
CNSS总分（分）84.45±20.2471.31±26.71t=5.660<0.001
护理专业知识及实践（分）17.11±4.4715.18±5.78t=3.818<0.001
工作量与时间分配（分）13.41±3.4510.67±4.19t=7.268<0.001
工作环境及自愿（分）6.59±2.275.75±2.57t=3.549<0.001
病人护理（分）27.63±7.2023.03±8.99t=5.767<0.001
管理及人际关系（分）19.70±6.4416.69±7.40t=4.435<0.001
), ArticleFig(id=1322530508991484166, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322169035110863773, language=EN, label=Table 2, caption=

Assignment of variables

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变量赋值方式
是否来自三级医院是=0，否=1
性别男=0，女=1
科室儿内科=1，儿外科=2，儿科门急诊=3，PICU=4， NICU=5，儿科康复病房=6
学历专科及以下=1，本科=2，硕士研究生及以上=3
职称护士/护师=1，主管护师=2，副主任护师及以上=3
职务科护士长=1，病区护士长=2，带教老师=3，专科护士=4，无=5
婚姻状况已婚=1，未婚=2
子女数量无=1，1个=2，≥2个=3
每周工作时间≤40 h=1，41~48 h=2，≥49 h=3
月均收入<5 000元=1，5 000~10 000元=2，>10 000~15 000元=3，>15 000元=4
健康状况差=1，较差=2，一般=3，较好=4，好=5
工作-家庭冲突经常=1，有时=2，几乎没有=3
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变量赋值情况

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变量赋值方式
是否来自三级医院是=0，否=1
性别男=0，女=1
科室儿内科=1，儿外科=2，儿科门急诊=3，PICU=4， NICU=5，儿科康复病房=6
学历专科及以下=1，本科=2，硕士研究生及以上=3
职称护士/护师=1，主管护师=2，副主任护师及以上=3
职务科护士长=1，病区护士长=2，带教老师=3，专科护士=4，无=5
婚姻状况已婚=1，未婚=2
子女数量无=1，1个=2，≥2个=3
每周工作时间≤40 h=1，41~48 h=2，≥49 h=3
月均收入<5 000元=1，5 000~10 000元=2，>10 000~15 000元=3，>15 000元=4
健康状况差=1，较差=2，一般=3，较好=4，好=5
工作-家庭冲突经常=1，有时=2，几乎没有=3
), ArticleFig(id=1322530509117313288, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322169035110863773, language=EN, label=Table 3, caption=

Classification training set results of 3 models

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模型AUC准确度灵敏度特异度阳性预测值阴性预测值F1分数
Logistic回归0.7250.6730.4210.9090.8500.6310.547
极端梯度提升0.8900.7940.7310.8520.8320.7760.773
随机森林0.9030.8010.8130.7900.7890.8220.798
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训练集各模型分类结果

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模型AUC准确度灵敏度特异度阳性预测值阴性预测值F1分数
Logistic回归0.7250.6730.4210.9090.8500.6310.547
极端梯度提升0.8900.7940.7310.8520.8320.7760.773
随机森林0.9030.8010.8130.7900.7890.8220.798
), ArticleFig(id=1322530510862143754, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322169035110863773, language=EN, label=Table 4, caption=

Classification validation set results of 3 models

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模型AUC准确度灵敏度特异度阳性预测值阴性预测值F1分数
Logistic回归0.7110.6540.3860.9070.8170.6130.510
极端梯度提升0.8020.7130.6930.7320.7240.7200.700
随机森林0.8250.7410.7710.7120.7420.7830.740
), ArticleFig(id=1322530510941835531, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322169035110863773, language=CN, label=表4, caption=

验证集各模型分类结果

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模型AUC准确度灵敏度特异度阳性预测值阴性预测值F1分数
Logistic回归0.7110.6540.3860.9070.8170.6130.510
极端梯度提升0.8020.7130.6930.7320.7240.7200.700
随机森林0.8250.7410.7710.7120.7420.7830.740
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基于可解释性机器学习的儿科护士付出-回报失衡风险预测模型的构建
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陈正菊 , 张秀梅 , 邵鹏 *, *
护理研究 | 科研论著 2026,40(8): 1289-1297
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护理研究 |科研论著 2026 , 40 (8) : 1289 -1297
基于可解释性机器学习的儿科护士付出-回报失衡风险预测模型的构建
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陈正菊, 张秀梅, 邵鹏*, *
作者信息
  • 1安徽医科大学第一附属医院，安徽 230022
通讯作者:
邵鹏，E-mail：
SHAO Peng， E-mail：
作者简介:

陈正菊，主管护师，本科

Construction of the risk prediction model for pediatric nurses' effort-reward imbalance based on interpretable machine learning
Zhengju CHEN, Xiumei ZHANG, Peng SHAO
Affiliations
  • 1The First Affiliated Hospital of Anhui Medical University, Anhui 230022 China
    • Corresponding Author:
    SHAO Peng， E-mail：
出版时间: 2026-04-25 doi: 10.12102/j.issn.1009-6493.2026.08.007
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摘要
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目的

基于可解释性机器学习构建儿科护士付出-回报失衡的风险预测模型，比较不同模型的预测性能，并采用SHAP对最优模型结果进行解释。

方法

采用便利抽样法，于2025年6月选取安徽省、山西省、江西省、湖南省6所医院儿科护士414人作为研究对象，按照7∶3随机分为训练集和验证集。采用护士压力量表、付出-回报失衡量表进行调查。采用LASSO回归对特征变量进行筛选，得出重要预测因子。将重要预测因子纳入机器学习中，构建儿科护士付出-回报失衡的Logistic回归模型、极端梯度提升模型、随机森林模型3种风险预测模型，比较模型的受试者工作特征曲线下面积（AUC）、准确度、灵敏度、F1分数，评价模型的预测性能，筛选最优模型，采用SHAP对最优模型进行解释。

结果

LASSO回归筛选出每个月夜班数、工作量与时间分配、学历3个重要因子。Logistic回归模型、极端梯度提升模型、随机森林模型3种预测模型的AUC分别是0.725，0.890，0.903，准确度为0.673，0.794，0.801，灵敏度为0.421，0.731，0.813，F1分数为0.547，0.773，0.798。SHAP结果显示影响因素重要性排序为每个月夜班数、护士工作量与时间分配、学历。

结论

通过随机森林构建的儿科护士付出-回报失衡风险预测模型性能优于Logistic回归、极端梯度提升模型。应根据护士每个月夜班数、工作量与时间分配、学历进行个性化预测，为其付出-回报失衡的早期识别、制定个性化干预措施提供参考。

儿科护士  /  付出-回报失衡  /  机器学习  /  LASSO回归分析  /  影响因素
Objective

To construct the risk prediction model for pediatric nurses' effort-reward imbalance based on interpretable machine learning,and to compare the predictive performance of different models.To explain the results of the optimal model using SHAP interpretation.

Methods

Using the convenience sampling method,a total of 414 pediatric nurses from 6 hospitals in Anhui province,Shanxi province,Jiangxi province and Hunan province in June 2025 were selected as the research subjects.They were randomly divided into training set and validation set at a ratio of 7∶3.The Chinese Nurse Stressor Scale and the Effort-Reward Imbalance Questionnaire scale were used for investigation.LASSO regression was employed to screen the characteristic variables and identify the important predictors.The important predictors were incorporated into the machine learning model to construct three risk prediction models for pediatric nurses' effort-reward imbalance:Logistic regression model,Extreme Gradient Boosting model,and Random Forest model.The areas under the receiver operating characteristic curves(AUC),accuracy,sensitivity,and F1 score of the models were compared to evaluate the predictive performance of the models and select the optimal model.The SHAP explanation was used to interpret the optimal model.

Results

LASSO regression identified three important factors:the number of night shifts per month,workload and time allocation,and educational background.The AUC values of the three prediction models(Logistic regression model,Extreme Gradient Boosting model,and Random Forest model) were 0.725,0.890,and 0.903 respectively,with accuracies of 0.673,0.794,and 0.801,sensitivities of 0.421,0.731,and 0.813,and F1 scores of 0.547,0.773,and 0.798.The SHAP explanation results showed that the importance ranking of the influencing factors were the number of night shifts per month,workload and time allocation,and educational background.

Conclusions

The risk prediction model for pediatric nurses' effort-reward imbalance based on interpretable machine learning constructed by Random Forest has better performance than Logistic regression and Extreme Gradient Boosting model.Personalized predictions should be made based on the number of night shifts each nurse takes per month,workload and time allocation,and educational backgroun.It provides a reference for the early identification of the imbalance between effort and reward for the nurses and the formulation of personalized intervention measures.

pediatric nurses  /  effort-reward imbalance  /  machine learning  /  LASSO regression analysis  /  influencing factors
陈正菊, 张秀梅, 邵鹏. 基于可解释性机器学习的儿科护士付出-回报失衡风险预测模型的构建. 护理研究, 2026 , 40 (8) : 1289 -1297 . DOI: 10.12102/j.issn.1009-6493.2026.08.007
Zhengju CHEN, Xiumei ZHANG, Peng SHAO. Construction of the risk prediction model for pediatric nurses' effort-reward imbalance based on interpretable machine learning[J]. Chinese Nursing Research, 2026 , 40 (8) : 1289 -1297 . DOI: 10.12102/j.issn.1009-6493.2026.08.007
正文
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付出-回报失衡是指一种高付出低回报的工作状态，一般用于评估工作压力大小。这种失衡感会导致情绪疲惫和职业倦怠甚至造成抑郁的发生，进而影响其职业认同感及留职意愿[1-2]。儿科护理作为医疗体系中至关重要的一部分，承担着为儿童提供专业护理服务的重任。然而，儿科护士在日常工作中面临着众多挑战，包括高强度的工作负荷、情感投入及工作场所暴力等，这些挑战往往导致儿科护士在付出与回报之间出现失衡[3-5]。因此，明确儿科护士付出-回报失衡影响因素至关重要。目前关于儿科护士的付出-回报失衡的影响因素分析方法单一，缺乏多种方法综合评估的比较及个体化的评估方法。Logistic回归分析、极端梯度提升（XGBoost）、随机森林（Random Forest）作为机器学习中经典的算法已广泛应用到临床研究中[6-7]，但此3种算法各具优势，预测能力和模型性能也存在差异，本研究拟构建儿科护士付出-回报失衡的Logistic回归模型、极端梯度提升模型、随机森林模型，比较模型性能筛选最优模型。而由于机器习模型的复杂性，模型结果通常难以解释，融合夏普利加性解释（SHAP）框架的预测模型可弥补机器学习的“黑箱特性”,使预测结果可视化，便于理解[8-9]，本研究将通过SHAP解释对最优模型结果进行解释，旨在为护理管理者早期精准识别护士的付出-回报风险并及早采取个体化干预措施提供更加可靠的依据，从而保证护理队伍的稳定、健康发展。
1　对象与方法
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1.1　调查对象
采用便利抽样法，于2025年6月选取安徽省、山西省、江西省、湖南省6所不同等级医院儿科护士作为研究对象。纳入标准：医院在岗护士；在儿科工作时间≥1年；知情同意，愿意参加本次研究；既往无精神病史、意识障碍等。排除标准：调查期间因休假、进修等原因不在岗；调查时未单独值夜班的护士。剔除标准：答题不完全；答案前后矛盾；规律性作答；答题时间<2 min。样本量计算方法：采用问卷最大条目数的5~10倍计算样本量，付出-回报失衡量表共23个条目，考虑20%的无效问卷，计算出所需样本量为138~276例。本研究已通过医院伦理委员会批准（伦理号：安医一附院科研伦审-PJ 2025-06-49），且调查对象均已知情同意，自愿参加。
1.2　调查工具
1.2.1　一般资料调查表
采用自行设计的问卷收集儿科护士年龄、所在省市、所在医院等级、工龄、职称、职务、文化程度、婚姻状况、子女数量、每个月夜班数、月均收入、健康状况等信息。
1.2.2　护士压力量表（Chinese Nurse Stressor Scale,CNSS）
CNSS由李小妹等[10]设计，该量表包括护理专业知识及实践（7个条目）、工作量与时间分配（5个条目）、工作环境及自愿（3个条目）、病人护理（11个条目）、管理及人际关系（9个条目）5个维度，共35个条目。采用Likert 4级评分法，“无压力”到“压力非常严重”分别计1~4分。本量表的Cronbach's α系数为0.980。
1.2.3　付出-回报失衡量表（Effort-Reward Imbalance Questionnaire,ERI）
ERI由Li等[11]编制，该量表包括付出（6个条目）、回报(11个条目)、内在投入(6个条目)3个维度，共23个条目，采用Likert 5级评分法计分，“非常不同意”到“非常同意”分别计1~5分，付出-回报比=付出维度得分/（回报维度得分×X），其中X表示调整系数，是付出模块条目数与回报模块条目数的比值，即6/11；当付出回报比≤1表示付出-回报处于平衡状态，付出-回报比>1表示付出-回报处于失衡状态。内在投入又称“超负荷”，对付出-回报失衡起调节作用，内在投入得分越高，越会加剧失衡程度。本研究中付出-回报失衡量表中3个维度内部一致性的 Cronbach's α系数分别为 0.950，0.962，0.927，量表总效度KMO值为0.975。
1.3　资料收集与质量控制方法
采用问卷星发放问卷的方式进行样本收集，发放问卷前先发送电子邮件至医院护理部及儿科护士长处向其说明问卷的内容及目的，取得同意再进行问卷发放，问卷采用统一的指导语征得调查者同意并说明问卷填写方式及注意事项，限制每个账号只能填写1次。问卷回收后，双人进行核查，剔除答题时间<2 min、规律性作答问卷。本研究共发放问卷550份，回收问卷452份，剔除无效问卷最终回收有效问卷414份，有效回收率为75.27%。
1.4　统计学方法
采用python 3.11.4和SPSS 27.0软件完成统计分析。符合正态分布的定量资料以均数±标准差（x±s）表示，采用t检验；不符合正态分布的定量资料以中位数（四分位数）[M(P25,P75)]表示，采用Mann-Whitney U检验。定性资料用频数、百分比（%）表示，采用χ2检验。使用LASSO回归对变量进行选择和过滤。应用Logistic回归、极端梯度提升、随机森林3种机器学习算法建立预测模型，使用受试者工作特征（ROC）曲线评估不同模型在训练集和验证集的区分度。验证集的ROC曲线下面积（AUC）更能体现模型的泛化能力，以验证集的AUC值作为筛选最优模型的主要指标。应用SHAP方法对预测因子重要性排序，并给出模型最重要的预测因子。以P<0.05为差异有统计学意义。
2　结果
收起
2.1　儿科护士的一般资料及付出-回报失衡发生情况
414名儿科护士年龄（34.07±5.73）岁；儿科工作时间（10.65±6.22）年；每个月夜班数（6.84±6.28）个；儿内科279人，占67.39%；主管护师261人，占63.04%；护士长占7.97%，带教老师占10.14%，专科护士占8.70%；硕士研究生及以上学历21人，占5.07%；月均收入5 000~10 000元249人，占60.14%。付出-回报失衡的发生率为51.45%，儿科护士发生付出-回报失衡影响因素的单因素分析见表1。
2.2　儿科护士付出-回报失衡影响因素的LASSO回归分析
按照7∶3比例将儿科护士分为训练集（289例）和验证集（125例），以是否发生付出-回报失衡作为因变量，连续变量原值输入，对分类变量进行赋值，变量赋值见表2。采用LASSO回归分析从21个可能的影响因素中筛选，并采用交叉验证方法确定最佳的λ值，随着惩罚系数λ变化，自变量的系数被逐渐压缩（见图1），采用10折交叉验证方法确定最佳的λ值，当λ=0.073时，模型的性能最优（见图2），得出每个月夜班数、工作量与时间分配、学历3个重要预测因子。
2.3　儿科护士付出-回报失衡3种预测模型的构建
将LASSO回归分析中具有意义的自变量纳入，应用Logistic回归、极端梯度提升、随机森林构建儿科护士付出-回报失衡风险预测模型，并对不同模型进行性能比较。对3种机器学习模型性能的评价结果显示：随机森林模型的AUC高于Logistic回归、极端梯度提升模型。验证集的AUC更能体现模型的泛化能力，AUC的大小作为筛选最优模型的主要指标，结果表明：随机森林算法在3个模型中泛化能力最强，预测性能最高，可以作为最优模型，F1分数为精确率与召回率的调和平均数，随机森林在训练集与验证集中的 F1分数分别为0.798，0.740，表明该模型稳健性较好，见图3、图4、表3、表4。
2.4　模型的可解释性
基于随机森林模型特征对不同因素的SHAP值进行重要性排序，特征Shapley值越大，则对模型预测结果贡献越大。条形图显示，对儿科护士付出-回报失衡影响最大的特征是每个月夜班数，其次是护士工作量与时间分配和学历，见图5。散点图中蓝色散点大部分位于横轴左侧，红色散点多位于横轴右侧，可见每个月夜班数、工作量与时间分配及学历对模型的贡献均具有正向影响，夜班数越多、工作量与时间分配得分越高、学历越高越容易发生付出-回报失衡，见图6。交互式SHAP图为随机提取数据中单个护士的可视化结果，红色数值代表付出-回报失衡的风险贡献，蓝色数值代表非付出-回报失衡的贡献，箭头越长对结果贡献越大，箭头向左表示SHAP值减少，箭头向右表示SHAP值增加。护士非付出-回报失衡的SHAP值为0.23，影响其结果权重高的红色特征为工作量与时间分配，蓝色特征为学历，见图7。护士付出-回报失衡的SHAP值为1.00，影响其结果权重高的红色特征为每个月夜班数，见图8。
3　讨论
收起
3.1　儿科护士付出-回报失衡发生风险处于中等水平
本研究结果显示，414名儿科护士中发生付出-回报失衡213人，发生率为51.45%，处于中等水平,与王影珍等[12-13]研究结果相似。但相较于欧美国家仍处于较高水平[14]，世界卫生组织发布全球护士短缺主要集中在中低收入国家[15]。我国护士在工作量管理、工作时间安排、心理情绪调节方面付出巨大的努力[16]，导致付出-回报失衡水平高出发达国家。研究显示，儿科护士付出-回报失衡发生率仅次于急诊科、手术室护士，处于较高水平[17-19]。儿科护士所服务的对象为认知及表达能力均欠佳的儿童，病情变化迅速，护理人员长期处于高压状态，而相应的工资待遇却普遍偏低[12]。
3.2　儿科护士发生付出-回报失衡的影响因素
3.2.1　每个月夜班数多的儿科护士易发生付出-回报失衡
本研究结果显示，儿科护士每个月夜班数在6个左右，成为影响付出-回报失衡的重要因素。夜班工作是超负荷的轮班作业，不仅会干扰神经生理节律，还会影响工作能力及工作效率，使护士处于紧张状态，损害身心健康[20]，且儿童病情复杂多变，特别是儿科急诊及重症病房患儿。儿科护士夜班中不仅承担病情观察和急救的任务、面临儿童血管条件差穿刺困难的压力，同时面对情绪激动的家长经常受到指责和肢体暴力，而他们不能及时求助于同事，造成身体及精神双重压力，导致付出-回报失衡的发生率增加。所以，儿科护理管理者应加强儿科护士专业技能培训，提升沟通技巧，增强职业素养，提高夜班的应对能力，并完善应急系统建设，做好急救支援工作。同时，可以向管理层申请提高儿科护士夜班待遇，使护士感受到付出得到尊重，降低付出-回报失衡发生率。
3.2.2　工作量与时间分配得分高的儿科护士更容易发生付出-回报失衡
工作量与时间分配主要包括工作量太大、上班的护士数量少、没有时间对病人实施心理护理、非护理性的工作太多、无用的书面工作太多，此维度主要体现了护士在超负荷的工作与相对缺乏的人力资源之间的冲突，护士不得不加班完成工作量，易造成护士工作疲劳心理压力加大。首先，儿科工作烦琐，儿童表达能力和自理能力差，诊疗配合度低，需要护士付出较多的精力和耐心，特别是儿童重症病房为无陪病房，护士需要花大量时间评估患儿病情及需求，并提供生活护理、专科护理，同时需要完善相关护理评估单及文书记录。且不同于成年人科室，由于儿童的认知水平差异及家长较高的情感需求，儿科护士需要提供更多的心理护理和情感支持，导致较高的付出水平[21]。护理管理者在工作中应优化工作流程，提高工作效率，同时优化人员配置，弹性排班，减轻护士工作压力，从而降低儿科护士付出-回报失衡风险。
3.2.3　学历越高的儿科护士付出-回报失衡发生率更高
本研究结果显示，学历越高的儿科护士付出-回报失衡发生率越高，与陈婷婷等[22]研究结果一致，高学历护士是未来护理行业的支柱。学历高的护士对自身职业发展有更高的期待，而护理是一门实践性的学科，不仅需要掌握科学的理论知识，还需具备一定实践能力和专业行为素养，这使高学历护士产生一定的心理落差。且在做好临床工作的同时仍需要承担教学及科研工作，付出较多的休息时间和额外的精力，而在回报层面，儿科护士收入水平整体处于偏低水平，且存在紧张的护患关系[12,23]，较高的付出得不到预期的回报，造成付出-回报失衡的发生。因此，医院应合理配置人员，防止高学历护士因工作量过大而产生倦怠感，同时搭建高学历护士关怀和支持平台，引导其做好职业规划，使其感受到足够理解与尊重。护理管理者可以优化激励机制，做好工作强度调整、薪酬待遇分配、发展晋升空间的平衡[24]，从而降低付出-回报失衡的风险。
3.3　儿科护士付出-回报失衡风险预测模型中随机森林模型预测性能最佳
本研究利用LASSO回归分析对21个自变量进行压缩筛选，提取到最优特征集，将经过筛选的关键变量输入不同的机器学习算法构建预测模型。3种模型预测性能结果显示，随机森林预测儿科护士付出-回报失衡的AUC高于Logistic回归、极端梯度提升模型，可见随机森林预测性能最佳。Logistic回归模型广泛应用到二分类结局及危险因素的研究中，在本研究中有较高的特异性，然而此模型为线性模型[25]，而本研究中非线性数据较多，导致其灵敏度较低。极端梯度提升模型处理分类问题的效果好、运行速度快，具有较好的识别能力和拟合优度[26]，在本研究中性能略次于随机森林模型，可能由于本研究中数据分布存在偏差，随机森林可以通过多树平均化提升抗干扰能力，而极端梯度提升抗干扰能力不如随机森林强。随机森林模型基于Bagging原理，采用多个决策树的集成算法思想，通过增强多个弱分类器提升整个模型的性能，在处理中等规模数据中具有较好的稳健性，且能够降低数据过拟合的风险[27]，在本研究中具有较大的优势，但其仍然存在着“黑盒”问题，使其不能简单明了地显示各变量在模型中的贡献。因此，本研究通过SHAP值解释特征在模型中的贡献大小，得出影响儿科护士付出-回报失衡因素的变量重要性排序，依次是每个月夜班数、工作量与时间分配、学历，且本研究SHAP交互式算法展示了儿科护士单个个体发生付出-回报失衡的风险概率及每个影响因素的贡献程度，这样有助于为个体精准化预防儿科护士发生付出-回报失衡提供参考依据，促进精准预测与个体化干预的推广。
4　小结
收起
本研究基于机器学习构建的随机森林模型预测性能优于Logistic回归、极端梯度提升模型，儿科护士付出-回报失衡受每个月夜班数、工作量与时间分配、学历的影响，可解释的机器学习为儿科护士付出-回报失衡的风险及影响因素提供了新思路。本研究的局限性在于数据来源不均衡，儿科不同科室的样本量有一定差异，可能对最终结果造成一定的偏差，希望在今后纳入更多样本量，确保样本分布的均衡性，并进一步通过外部验证保证模型的可靠性。

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doi: 10.12102/j.issn.1009-6493.2026.08.007
  • 接收时间：2025-08-14
  • 首发时间：2026-10-28
  • 出版时间：2026-04-25
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  • 收稿日期：2025-08-14
  • 修回日期：2026-04-03
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    1安徽医科大学第一附属医院，安徽 230022

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2种不同金属材料的力学参数
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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