Ajou University repository

Clinical nurses’ work-life balance prediction due to patient safety incidents using classification and regression tree analysis: a secondary data analysisoa mark
Citations

SCOPUS

3

Citation Export

DC Field Value Language
dc.contributor.authorKang, Jiwon-
dc.contributor.authorKwon, Soon Sun-
dc.contributor.authorLee, Youngjin-
dc.date.issued2024-12-01-
dc.identifier.issn1472-6955-
dc.identifier.urihttps://dspace.ajou.ac.kr/dev/handle/2018.oak/33913-
dc.description.abstractBackground: 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.-
dc.description.sponsorshipThis study was financially supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (NRF2019R1A2C1085924), the Center for Women in Science, Engineering, and Technology. Grant funded by the MIST under the Program for Returners into R&D (WISET-2020\u2013561), Basic Science Research Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2021R1A6A1A10044950), and the Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (RS-2023\u201300255968) grant funded by the Korea government (MSIT).-
dc.language.isoeng-
dc.publisherBioMed Central Ltd-
dc.titleClinical nurses’ work-life balance prediction due to patient safety incidents using classification and regression tree analysis: a secondary data analysis-
dc.typeArticle-
dc.citation.titleBMC Nursing-
dc.citation.volume23-
dc.identifier.bibliographicCitationBMC Nursing, Vol.23-
dc.identifier.doi10.1186/s12912-024-01719-0-
dc.identifier.scopusid2-s2.0-85182984451-
dc.identifier.urlhttps://bmcnurs.biomedcentral.com/-
dc.subject.keywordOccupational stress-
dc.subject.keywordPatient safety-
dc.subject.keywordSafety management-
dc.subject.keywordWork-life balance-
dc.description.isoatrue-
dc.subject.subareaNursing (all)-
Show simple item record

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Kwon, Soon-sun Image
Kwon, Soon-sun권순선
Department of Mathematics
Read More

Total Views & Downloads

File Download

  • There are no files associated with this item.