Influence of Clinical Nurses’ Second-Victim Experience and Second-Victim Support in Relation to Patient Safety Incidents on Their Work-Related Outcomes

Jung, Su Jin; Lee, Youngjin; Bae, Sun Hyoung

doi:10.11111/jkana.2022.28.4.331

Cited by 4 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Second-victim Experience and Support Tool (SVEST) developed by Burlison et al [ 5 ] to assess second-victims’ experiences, second-victims’ support systems, and negative work-related PSI outcomes among healthcare providers were used after obtaining permission from the developer and Korean validation research’s author [ 19 ], who adapted the tool. In this study, 29 items of the original tool were equally measured.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, 29 items of the original tool were equally measured. Based on the previous study [ 19 ], the seven dimensions’ scores of the 29 items were finally divided into three subdomain total scores (second-victim experience, second-victim support, and third-victim experience). The tool includes reverse-coded items, and uses a 5-point Likert scale, from 1 “strongly disagree” to 5 “strongly agree.” A higher score indicates greater second-victim and third-victim experiences.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Kang,

Kwon,

Lee

2024

BMC Nurs

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Kang,

Kwon,

Lee

2024

BMC Nurs

Self Cite

View full text Add to dashboard Cite

show abstract

“…실제로 신규간호사의 환자안전사건 발생률은 경력 간호사보 다 크게는 3배 가까이 높다고 보고된다 [10]. 환자안전사건을 경험한 신규간호사는 전문가로서의 자신감을 상실하고, 환자 들에게 죄책감을 느끼며, 동료들의 질타를 두려워하여 결국에 는 이직을 고려한다 [11,12] [19]. 뿐만 아니라 시뮬레이션 교육은 간호대학생과 임상간호사의 지식 [17], 의사소통능력 [20], 문제해결능력 [20], 임상수행능력 [21] 향상에 효과적인 교육 방법으로 보고되고 있다.…”

Section: 서 론unclassified

Development of a Patient Safety Simulation Program for New Nurses in the Intensive Care Unit

Jung,

Park

2024

J Korean Acad Fundam Nurs

Self Cite

View full text Add to dashboard Cite

Purpose: Intensive care units (ICUs) are departments that use high-risk drugs and medical devices. Therefore, patient safety is especially important in ICUs. However, new nurses in ICUs often lack patient safety competencies. Thus, it is necessary to improve patient safety competencies for new nurses working in ICUs.Methods: This methodological study was designed to develop a simulation program to improve patient-safety competencies among new nurses working in ICUs. To develop a patient-safety simulation program, we conducted a survey on educational needs and reviewed the core competencies of patient safety applicable to healthcare providers. Additionally, we conducted an expert validation process and a pilot test.Results: A simulation-based patient safety program was developed that spanned 4 weeks (240 minutes).Conclusion: The patient safety simulation program, which considered the characteristics and competence levels of new nurses working in ICUs, was found to be appropriate. This program is expected to improve the patient safety competencies of new intensive care nurses, thereby contributing to the creation of a safer ICU environment.

show abstract

“…The Second-victim Experience and Support Tool (SVEST) developed by Burlison et al [5] to assess second-victims' experiences, second-victims' support systems, and negative work-related PSI outcomes among healthcare providers were used after obtaining permission from the developer and Korean validation research's author [17], who adapted the tool. In this study, 29 items of the original tool were equally measured.…”

Section: Measurementsmentioning

confidence: 99%

“…In this study, 29 items of the original tool were equally measured. Based on the previous study [17], the seven dimensions' scores of the 29 items were nally divided into three subdomain total scores (second-victim experience, second-victim support, and thirdvictim experience). The tool includes reverse-coded items, and uses a 5-point Likert scale, from 1 "strongly disagree" to 5 "strongly agree."…”

Section: Measurementsmentioning

confidence: 99%

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

Kang,

Kwon,

Lee

2023

Preprint

Self Cite

View full text Add to dashboard Cite

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 data were analyzed using Lasso. 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 results can be used as fundamental data for formulating workforce risk management strategies, such as managing each nurse’s occupational stress. Ultimately, they can help improve organizational culture to prevent the recurrence of additional patient safety incidents.

show abstract

Influence of Clinical Nurses’ Second-Victim Experience and Second-Victim Support in Relation to Patient Safety Incidents on Their Work-Related Outcomes

Cited by 4 publications

References 22 publications

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

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

Development of a Patient Safety Simulation Program for New Nurses in the Intensive Care Unit

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

Contact Info

Product

Resources

About