倦怠
同情疲劳
劳动力
医学
工作满意度
护理部
横断面研究
同情
情绪衰竭
逻辑回归
心理学
家庭医学
临床心理学
社会心理学
病理
法学
经济
政治学
内科学
经济增长
作者
Abdelrahman Salameh,Bushra M. Ghannam,Omar Melhem,Rasha Kadri Ibrahim,Jafar Alasad Alshraideh
标识
DOI:10.1177/23779608251352792
摘要
Background The global nursing shortage continues to strain healthcare systems, with the intention to leave (ITL), which is defined as the likelihood of leaving one's job or profession, emerging as a key contributor to workforce instability. Objective The study aims to assess the correlation between sociodemographic characteristics, professional quality of life (ProQoL), critical care nurses’ intention to leave their units, and their intention to leave the nursing profession. Methods A cross-sectional descriptive study of 135 critical care nurses in Jordan was conducted between February and May 2024 using an electronic questionnaire that included ITL and ProQoL Version 5. Associations between variables were examined using point-biserial correlation and the Chi-square test. A logistic regression was performed to ascertain the effects of ProQoL and demographics on the likelihood that nurses intended to leave. Results Of the 135 nurses surveyed, 56% reported an intention to leave the nursing profession, and 55% expressed intent to leave their current critical care units. Most participants demonstrated moderate levels of compassion satisfaction (85%), compassion fatigue (86%), and burnout (94%). Female nurses reported higher levels of compassion satisfaction, burnout, and compassion fatigue than males. Regression analysis showed that intention to leave the profession was significantly predicted by intention to leave the unit ( B = −2.268, p < .05), though burnout was not a significant predictor. Conclusions Burnout and compassion fatigue were significantly predictive of ITL, while compassion satisfaction mitigated its likelihood. These findings demonstrate the necessity of proactive and strategic policies designed to address nurses’ physical and mental health conditions and restructure their staffing and scheduling frameworks to retain critical care nurses.
科研通智能强力驱动
Strongly Powered by AbleSci AI