Stressors and level of stress among different nursing positions and the associations with hyperlipidemia, hyperglycemia, and hypertension: a national questionnaire survey.

BACKGROUND: Nurses are faced with varying job stressors depending on their positions and duties. Few previous studies have compared job stress and related chronic conditions among different nursing positions. The objectives were to compare job stressors among clinical registered nurses, nurse practitioners, and head nurses and explore the impact of job stressors and stress level on hyperlipidemia, hyperglycemia, and hypertension. METHODS: Secondary data extracted from a survey of health-care workers conducted from May to July 2014 across 113 hospitals in Taiwan was used. This analysis included 17,152 clinical registered nurses, 1438 nurse practitioners, and 2406 head nurses. Socio-demographic characteristics, job stressors, stress levels, and hyperlipidemia, hyperglycemia, and hypertension variables were extracted. RESULTS: Perceived stressors differed among clinical registered nurses, nurse practitioners, and head nurses, but overall stress level did not. Nurse practitioners and head nurses showed significantly higher prevalence of hyperlipidemia, hyperglycemia, and hypertension than clinical registered nurses. Higher stress levels, age, body mass index, work hours, and caring for family members were positively associated with hyperlipidemia, hyperglycemia, and hypertension. After adjustment for these variables, risk of hyperlipidemia, hyperglycemia, and hypertension did not differ across the nursing positions. CONCLUSIONS: Although stressors vary by different nursing positions, overall stress level does not. Hyperlipidemia, hyperglycemia, and hypertension are related to stress level, age, body mass index, weekly working hours, and caring for family members. Hence, alleviating job stress and avoiding long working hours are likely to reduce the risk of hyperlipidemia, hyperglycemia, and hypertension in nurses.

From job stress to intention to leave among hospital nurses: A structural equation modelling approach.

AIMS: The aim of this study was to examine the structural relationships linking job stress to leaving intentions through job satisfaction, depressed mood and stress adaptation among hospital nurses. BACKGROUND: High turnover among nurses is a global concern. Structural relationships linking job stress to leaving intentions have not been thoroughly examined. DESIGN: Two nationwide cross-sectional surveys of full-time hospital staff in 2011 and 2014. METHODS: The study participants were 26,945 and 19,386 full-time clinical nurses in 2011 and 2014 respectively. Structural equation modelling was used to examine the interrelationships among the study variables based on the hypothesized model. We used cross-validation procedures to ensure the stability and validity of the model in the two samples. RESULTS: There were five main paths from job stress to intention to leave the hospital. In addition to the direct path, job stress directly affected job satisfaction and depressed mood, which in turn affected intention to leave the hospital. Stress adaptation mitigated the effects of job stress on job satisfaction and depressed mood, which led to intention to leave the hospital. Intention to leave the hospital preceded intention to leave the profession. Those variables explained about 55% of the variance in intention to leave the profession in both years. CONCLUSION: The model fit was good for both samples, suggesting validity of the model. Strategies to decrease turnover intentions among nurses could focus on creating a less stressful work environment, increasing job satisfaction and stress adaptation and decreasing depressed mood. Hospitals should cooperate in this issue to decrease nurse turnover.

Long work hours and chronic insomnia are associated with needlestick and sharps injuries among hospital nurses in Taiwan: A national survey.

BACKGROUND: Needlestick and sharps injuries are prevalent work-related injuries among nurses. Safety devices prevent only one-quarter of related injuries. More studies of modifiable risk factors are needed. OBJECTIVES: To examine whether long work hours and chronic insomnia are associated with needlestick and sharps injuries among hospital nurses in Taiwan. DESIGN: Cross-sectional survey. SETTINGS/PARTICIPANTS: This analysis included 19,386 full-time bedside nurses working in 104 hospitals across Taiwan. METHODS: Participants filled out an anonymous questionnaire from July to September 2014. Chronic insomnia, needlestick injuries, and sharps injuries during the past year were each measured by a yes/no question. Multivariate logistic regression models were applied to examine the effects of long work hours and chronic insomnia on needlestick and sharps injuries, given with control for sex, marital status, educational level, age, years of practice, work unit, and hospital level in the model. RESULTS: More than 70% of study nurses worked long hours during the previous week (>50h: 27.5%; 41-50h: 43.2%), and 15.5% of nurses reported chronic insomnia. The percentage of sharps injuries (38.8%) was higher than that for needlestick injuries (22.4%) during the previous year among nurses. After adjusting for potential confounders, logistic regression yielded significant results showing that those who worked 41 to 50h per week, >50h per week, and had chronic insomnia were 1.17 times (95% C.I.=1.04-1.32), 1.51 times (95% C.I.=1.32-1.72), and 1.45 times (95% C.I.=1.25-1.68) more likely to experience needlestick injuries, and 1.29 times (95% C.I.=1.17-1.42), 1.37 times (95% C.I.=1.23-1.53), and 1.56 times (95% C.I.=1.37-1.77) more likely to experience sharps injuries, respectively, than those who worked fewer hours and did not have insomnia. CONCLUSIONS: This nationwide nurse survey showed that high rates of needlestick and sharps injuries persist in hospital nurses in Taiwan. The common problems of long work hours and chronic insomnia increase the risk of these injuries. We suggest that hospital managers follow regulations on work hours and optimize shift schedules for nurses to decrease related injuries.