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Your search keyword '"*STRUCTURAL equation modeling"' showing total 2 results
2 results on '"*STRUCTURAL equation modeling"'

1. How Robots Help Nurses Focus on Professional Task Engagement and Reduce Nurses' Turnover Intention.

Author

Chang, Hao‐Yuan, Huang, Tzu‐Ling, Wong, May‐Kuen, Ho, Lun‐Hui, Wu, Chieh‐Ni, and Teng, Ching‐I

Subjects
*HOSPITALS, *OCCUPATIONAL roles, *STRUCTURAL equation modeling, *EVALUATION of medical care, *NURSES' attitudes, *NURSING, *HEALTH status indicators, *CONTINUING education units, *JOB involvement, *ROBOTICS, *LABOR turnover, *HOSPITAL nursing staff, *DESCRIPTIVE statistics, *RESEARCH funding, *JOB satisfaction, *NURSES, *FACTOR analysis, *INTENTION, *DATA analysis software, *STATISTICAL correlation, *PERSONNEL management
Abstract

Purpose: To examine how robot‐enabled focus on professional task engagement and robot‐reduced nonprofessional task engagement are related to nurses' professional turnover intention. Design: We adopted a two‐wave study design. Methods: We collected the first wave of data in a large hospital in Taiwan during October and November 2019 and the second wave between December 2019 and February 2020. We used the data collected from 331 nurses who participated in both waves. Findings: We found that robot‐enabled focus on professional task engagement is positively related to nurses' overall job satisfaction and perceived health improvement. Robot‐reduced nonprofessional task engagement is positively related to nurses' perceived health improvement. Both overall job satisfaction and perceived health improvement are negatively related to nurses' professional turnover intention. Conclusions: Robots' ability to focus nurses' efforts in professional tasks may help improve nurses' health and overall job satisfaction, and by extension reduce their turnover intention. Clinical Relevance: Nurse managers could suggest hospitals introduce robots, particularly those that can share nurses' nonprofessional workload. This, meanwhile, could focus nurses' efforts on professional task engagement. [ABSTRACT FROM AUTHOR]

Published
2021
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2. The Influence of Work-Related Fatigue, Work Conditions, and Personal Characteristics on Intent to Leave Among New Nurses.

Author

Liu, Yi, Wu, Li‐Min, Chou, Pi‐Ling, Chen, Mei‐Hsin, Yang, Li‐Chien, and Hsu, Hsin‐Tien

Subjects
*PSYCHOLOGICAL burnout, *CHI-squared test, *FATIGUE (Physiology), *GOODNESS-of-fit tests, *INTENTION, *QUESTIONNAIRES, *RESEARCH funding, *STATISTICAL sampling, *SCALE analysis (Psychology), *STATISTICS, *WORK environment, *EMPLOYEE retention, *GRADUATES, *STRUCTURAL equation modeling, *CROSS-sectional method, *DATA analysis software, *DESCRIPTIVE statistics
Abstract

Purpose This study aimed to (a) test the fit of the hypothesized model for new nurses' intent to leave and (b) determine the extent to which personal characteristics, work conditions, and work-related fatigue predict intent to leave among new nurses. Design and Methods This study was a cross-sectional survey study. A total of 162 new nurses were recruited. A hypothesized model was proposed for model testing. Structural equation modelling was used for data analysis. Findings Work conditions only had an effect through work-related fatigue on new nurses' intent to leave. Personal characteristics did not have a significant effect on new nurses' intent to leave. The final model showed a good fit. Work-related fatigue, work conditions, and health explained 65% of the variance in new nurses' intent to leave. Conclusions Work-related fatigue was a major determinant of new nurses' intent to leave. More attention should be paid to fatigue reduction strategies among new nurses. Clinical Relevance Work-related fatigue should be monitored, particularly for new nurses who work more than 10 hr per day and who have greater workloads. [ABSTRACT FROM AUTHOR]

Published
2016
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