Aims: This study investigated health problems, turnover intention, and actual turnover among shift work nurses. While turnover intention is often used as a proxy variable for turnover, the relationship between these variables requires clarification. This study tested for relevant associations using prospective longitudinal data with a time lag of 12 months. We also tested for associations between health problems (sleep disturbance, fatigue, and depression) and turnover intentions/turnover, with a focus on the mediating role of turnover intention. Methods: This study conducted a secondary analysis of data from the Shift Work Nurses’ Health and Turnover project, which is a prospective longitudinal cohort study. We analyzed health problems, turnover intention, and actual turnover. The data were analyzed via descriptive statistics, the Pearson’s chi-squared test, independent t-test, univariable logistic regression, multiple logistic regression, and causal mediation. Results: Participants included 491 shift work female nurses. Of these, 112 (22.8%) had turnover intention, while 38 (7.7%) left their jobs within the 12-month period of investigation. Of the 112 with turnover intention, 22 left their jobs (OR 5.68. 95% CI 2.84–11.36). The logistic regression analysis showed that sleep disturbance and fatigue were associated with turnover intentions and actual turnover, while depression was only associated with turnover intention. The causal mediation analysis showed that turnover intention mediated the relationship between health problems (sleep disturbance and fatigue) and actual turnover (sleep disturbance OR 1.31, 95%CI = 1.02–1.60; fatigue OR 2.11, 95%CI = 1.50–2.68); sleep disturbance had a natural direct effect on actual turnover (OR 2.66, 95%CI,2.07–3.21). Conclusion: Turnover intention strongly predicted actual turnover. Sleep disturbances may result in turnover, even in cases without existing turnover intention. These findings highlight the need for early interventions aimed at preventing and alleviating sleep disturbances for shift work female nurses.

Shift work nurses experience multiple health problems due to irregular shifts and heavy job demands. However, the comorbidity patterns of nurses’ health problems and the association between health problems and turnover intention have rarely been studied. This study aimed to identify and cluster shift work nurses’ health problems and to reveal the associations between health problems and turnover intention. In this cross-sectional study, we analyzed data from 500 nurses who worked at two tertiary hospitals in Seoul, South Korea. Data, including turnover intention and nine types of health issues, were collected between March 2018 and April 2019. Hierarchical clustering and multiple ordinal logistic regressions were used for the data analysis. Among the participants, 22.2% expressed turnover intention and the mean number of health problems was 4.5 (range 0–9). Using multiple ordinal logistic regressions analysis, it was shown that sleep disturbance, depression, fatigue, a gastrointestinal disorder, and leg or foot discomfort as a single health problem significantly increased turnover intention. After clustering the health problems, four clusters were identified and only the neuropsychological cluster—sleep disturbance, fatigue, and depression—significantly increased turnover intention. We propose that health problems within the neuropsychological cluster must receive close attention and be addressed simultaneously to decrease nurse’s turnover intentions.

Purpose The premenstrual symptoms (PMSs) are a common health issue affecting women of reproductive age and are associated with various physiological, psychological, and situational factors. Newly graduated nurses, when suddenly exposed to excessive workloads and shift work, may experience disturbances such as sleep disorders, fatigue, depression, and stress. These factors are related to PMS, and their occurrence varies. Therefore, this study aimed to understand the changes in PMS and its risk factors among newly graduated nurses across shift work periods, and investigate the longitudinal relationships between PMS and its risk factors based on the theory of unpleasant symptoms. Methods This study constituted a secondary data analysis and employed a prospective longitudinal design. Newly graduated nurses were assessed three times: before starting shift work, six months after initiating shift work, and 18 months thereafter. Physiological (age, body mass index, alcohol consumption, dietary behavior), psychological (sleep quality, fatigue, depression, stress, life satisfaction), and situational (social support) factors were included as PMS-related factors. Results PMS worsened six months after the initiation of shift work compared with those before, and this aggravation persisted even after 18 months of shift work (p < .001). Age (p = .043), sleep quality (p = .004), and fatigue (p < .001) were associated with PMS. An interaction between time and depression that affected PMS was observed (p = .021). Conclusion Newly graduated nurses need to pay attention to the exacerbation of PMS after initiating shift work. Interventions to improve sleep quality and reduce fatigue are crucial for novice nurses, irrespective of shiftwork duration. Additionally, it is important to assess depressive symptoms before starting shift work and implement early interventions. Furthermore, the need for interventions addressing depression escalates with the increase in shiftwork duration. (c) 2024 Korean Society of Nursing Science. Published by Elsevier BV. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Purpose: This study investigated the dietary behavior of experienced shiftwork nurses and aimed to identify factors related to dietary behavior. Methods: This study was a secondary analysis based on the Shift Work Nurses’ Health and Turnover study among Korean nurses. In total, 247 experienced shiftwork nurses were included in this study. The participants’ dietary behavior, depression, level of occupational stress, fatigue, physical activity, and general characteristics were measured. Using SPSS ver. 28.0, data were analyzed to obtain descriptive statistics and Pearson correlation coefficients. The independent t-test, one-way analysis of variance, the Kruskal-Wallis test, and multiple regression analysis were also conducted. Results: The dietary behavior score of the participants using the Mini-Dietary Assessment Index was 29.35±5.67. Thirty percent of the participants were depressed, the participants experienced moderate occupational stress, and 74.1% of the participants engaged in an inadequate amount of physical activity. The factors influencing shiftwork nurses’ dietary behavior were having child(ren) (β=.16, p=.027), depression (β=–.13, p=.032), level of occupational stress related to occupational climate (β=–.13, p=.035), and an inadequate amount of physical activity (β=–.17, p=.006). These factors explained 10.4% of the variance in experienced shiftwork nurses’ dietary behavior scores. Conclusion: Experienced nurses with child(ren) tended to have healthier diets. However, a higher level of occupational stress related to occupational climate, depression, and engaging in an inadequate amount of physical activity were associated with a higher risk of having an unhealthy diet. Therefore, strategies are needed to encourage physical activity and alleviate adverse occupational climate and depression among experienced nurses.

OBJECTIVES: In South Korea, there are two nationwide health surveys conducted by the Korea Centers for Disease Control and Prevention: the Korea Community Health Survey (KCHS) and Korea National Health and Nutrition Examination Survey (KNHANES). The two surveys are directly comparable, as they have the same target population with some common items, and because both surveys are used in various analyses, identifying the similarities and disparities between the two surveys would promote their appropriate use. Therefore, this study aimed to compare the estimates of six variables in KCHS and eight variables in KNHANES over a six-year period and compare time series stability of region-specific and sex- and age-specific subgroup estimates.METHODS: Data from adults aged 19 years or older in the 2010-2015 KCHS and KNHANES were examined to analyze the differences of estimates and 95% confidence interval for self-rated health, current smoking rate, monthly drinking rate, hypertension diagnosis rate, diabetes diagnosis rate, obesity prevalence, hypertension prevalence, and diabetes prevalence. The variables were then clustered into subgroups by city as well as sex and age to assess the time series stability of the estimates based on mean square error.RESULTS: With the exception of self-rated health, the estimates taken based on questionnaires, namely current smoking rate, monthly drinking rate, hypertension diagnosis rate, and diabetes diagnosis rate, only differed by less than 1.0%p for both KCHS and KNHANES. However, for KNHANES, estimates taken from physical examination data, namely obesity prevalence, hypertension prevalence, and diabetes prevalence, differed by 1.9-8.4%p, which was greater than the gap in the estimates taken from questionnaires. KCHS had a greater time series stability for subgroup estimates than KNHANES.CONCLUSIONS: When using the data from KCHS and KNHANES, the data should be selected and used based on the purpose of analysis and policy and in consideration of the various differences between the two data.