基于决策树模型的机器人辅助手术专科护士核心胜任力的影响因素分析

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中图分类号 R472.3R687.1 文献标识码 A文章编号 2096-7721（2026）01-0140-07

AbstractObjective：Toconstructand validateadecisiontree prediction modelfor thecorecompetencies ofrobot-assistedsurgery specialtynursesndtodenifykeifuecinfctorsndeirinteactio.Methods：Acosecalstudydesinsdopted. Usingconvenencesampling toenroll26robot-asstedsurgeryspecialtynursesfromShanghai SixthPeople'sHospitaletweenMarch 2023and March2O25.Corecompetencywasevaluatedusingavalidatedcorecompetencyassessmentquestionnaire.Diferencesincore competencylevelsacrossnursecharacteristicsreanalyedivariateearsoncorelatioanalysis，ultiplelinearregioad decisiontreewreusedtoidentifyfactors influencingcorecompetencyResults：Amongthe6Onursesoupiedinorthopedicrobot assistedsurgrygasofkpcutialnt，altngdd and professional title were significant influencing factors on core competency levels （P<0.05） .Specifically，nursesaged>45years，with work experience>2O years，with highest education level of Master’s degree or higher， with ⩾3 external learning experiences， having undergone further training，andholdingadeputychief nurseorhigher title showed significantly highercorecompetencyscores（ P <0.05）. Pearsoncorrelationalissodthatageyearsofokexperieegsducatioalatainent，profeioaltitle，frcyof external training，and advanced study experience were positively correlated with various core competency scores （ >0 ， P<0.05 ）. Multiple linearegressionanalysisfurthercofirmedtatthesefactorswereindependentinfluencingfactorsforthecorecompetenciesofnurses specialized in robot-assisted surgery （ P <0.05）.The decision tree model indicated that the frequency of external training，advanced study experience，ighstducatioalaent，andproesioaltitleereindependentifeningactorsfooreompes，ith thefrequencyofextealtraininghavingthemostsignificantimpact.Conclusion：Age，yearsofork experiene，highesteducational attainment，profesioalitlefreucyoftealtringnddancddexperieretinfctorsifueningteoe competenciesofrobot-assistedsurgeryspecialtynurses.Clinicalpracticecanenancetecorecompetencyofrobot-asstedsurgery specialtynursestroughmultiplepathaysandhierarchicaltrainingapproachs，therebyimprovingtheeficiencyofothopedicrobot assisted surgery and patient outcomes.

KeyWordsRobot-assisted Surgery;Nurses;Competency;Prediction;Decision Tree

随着智能医疗技术的迅速发展，机器人辅助手术凭借其精准度高、创伤小、术后恢复快等优势，已成为骨科手术的重要发展方向[1-2]。（剩余10220字）