Background/Objectives: The nursing work environment, encompassing accessible resources and established processes, might affect nurses’ professional behavior. Our aim was to examine the effect of nurses’ work environments on quiet quitting and work engagement among nurses. Methods: We performed a cross-sectional study with nurses in Greece. We used the “Practice Environment Scale-5” to measure nurses’ work environments, the “Quiet Quitting Scale” to measure quiet quitting, and the “Utrecht Work Engagement Scale-3” to measure work engagement among nurses. We developed multivariable regression models adjusted for gender, age, understaffed wards, shift work, and work experience. Results: The study population included 425 nurses. The mean age of the nurses was 41.1 years. After controlling for confounders, we found that lower nurse participation in hospital affairs, less collegial nurse–physician relationships, worse nursing foundations for quality of care, and lower levels of nurse manager ability, leadership, and support were associated with higher levels of quiet quitting among nurses. Moreover, our multivariable analysis identified a positive association between nurse manager ability, leadership, and support, collegial nurse–physician relationships, nursing foundations for quality of care, and work engagement among nurses. Conclusions: Our findings highlight the poor work environment, elevated levels of quiet quitting, and moderate work engagement among nurses. Moreover, we found that a poor nurses’ work environment was associated with higher levels of quiet quitting. Moreover, our findings showed that nurses’ work environments had a positive impact on work engagement. The ongoing endeavor to enhance all aspects of nurses’ working conditions by healthcare organization administrations is essential for optimizing nurses’ performance, facilitating organizational operations, and ensuring service quality.
<abstract><sec> <title>Background</title> <p>Emotional intelligence can improve nurses' interpersonal and coping skills, job performance, and resilience. However, there is a dearth in the literature on whether emotional intelligence affects levels of quiet quitting, turnover intention, and job burnout in nurses.</p> </sec><sec> <title>Objective</title> <p>We examined the relationship between emotional intelligence, quiet quitting, turnover intention, and job burnout.</p> </sec><sec> <title>Methods</title> <p>We conducted a cross-sectional study in Greece with a convenience sample of 992 nurses. We used the following valid tools to measure our study variables: the Trait Emotional Intelligence Questionnaire-Short Form, the Quiet Quitting Scale, and the single item burnout measure.</p> </sec><sec> <title>Results</title> <p>The mean age of our nurses was 42.2 years. After controlling for gender, age, work experience, shift work, and understaffed department, the multivariable linear regression models indicated significant negative relationships between emotional intelligence and quiet quitting, turnover intention, and job burnout. Specifically, self-control reduced detachment, lack of motivation, job burnout, and turnover intention. Moreover, emotionality reduced detachment, lack of motivation, and lack of initiative. Sociability reduced lack of initiative and lack of motivation, while well-being reduced lack of motivation, job burnout, and turnover intention.</p> </sec><sec> <title>Conclusion</title> <p>Emotional intelligence reduced quiet quitting, turnover intention, and job burnout in nurses. Therefore, nurse managers and policy-makers should apply interventions to optimize the emotional intelligence profiles of nurses.</p> </sec></abstract>
<sec><title>Introduction</title><p>High workloads among nurses affect critical workplace outcomes, such as turnover intention, job burnout, and job satisfaction. However, there are no studies that measure the relationships between workload and these variables in the post-COVID-19 era.</p></sec><sec><title>Objective</title><p>To evaluate the effect of workload on quiet quitting, turnover intention, and job burnout.</p></sec><sec><title>Methods</title><p>We conducted a cross-sectional study using a sample of nurses in Greece. The NASA task load index was used to measure workloads among nurses. Also, we used valid scales to measure quiet quitting (quiet quitting scale), job burnout (single item burnout measure), and turnover intention (a six-point Likert scale).</p></sec><sec><title>Results</title><p>The mean workload score was 80.7, indicating high workloads in our sample. Moreover, most of the nurses belonged to the group of quiet quitters (74.3%). About half of the nurses reported a high level of turnover intention (50.2%). After controlling for confounders, data analysis showed that higher workloads were associated with higher levels of quiet quitting [beta = 0.009, 95% confidence interval (CI) = 0.006 to 0.012, p-value < 0.001], turnover intention (odds ratio = 1.046, 95% CI = 1.035 to 1.056, p-value < 0.001), and job burnout (beta = 0.072, 95% CI = 0.065 to 0.079, p-value < 0.001).</p></sec><sec><title>Conclusion</title><p>We found that workload was associated with quiet quitting, turnover intention, and job burnout in nurses. Thus, appropriate interventions should be applied to reduce nursing workloads to improve productivity and the healthcare provided to patients.</p></sec>
Abstract Background In general, COVID-19 vaccines are safe and effective, but minor adverse effects are common. Objective To estimate the prevalence of adverse effects after the first COVID-19 booster dose, and to identify possible risk factors. Material and methods We conducted a cross-sectional study with a convenience sample in Greece during November 2022. We measured several adverse effects after the booster dose, such as pain at the injection site, swelling at the injection site, fatigue, muscle pain, headaches, fever, chills, nausea, etc. We considered gender, age, chronic disease, self-assessment of health status, COVID-19 diagnosis, and self-assessment of COVID-19 course as possible predictors of adverse effects. Results In our sample, 96% developed at least one adverse effect. Half of the participants (50.2%) developed one to five adverse effects, 35.9% developed six to ten adverse effects, and 9.5% developed 11 to 16 adverse effects. Mean number of adverse effects was 5.5. The most frequent adverse effects were pain at the injection site (84.3%), fatigue (70.8%), muscle pain (61%), swelling at the injection site (55.2%), headache (49.8%), fever (42.9%), and chills (41%). Females developed more adverse effects than males (p<0.001). Also, we found a positive relationship between severity of COVID-19 symptoms and adverse effects of COVID-19 vaccines (p=0.005). Moreover, younger age was associated with increased adverse effects (p<0.001). Conclusions Almost all participants in our study developed minor adverse effects after the booster dose. Female gender, worse clinical course of COVID-19, and decreased age were associated with increased adverse effects.
The aim of the study was to examine the impact of moral resilience on quiet quitting, job burnout, and turnover intention among nurses. A cross-sectional study was implemented in Greece in November 2023. The revised Rushton Moral Resilience Scale was used to measure moral resilience among nurses, the Quiet Quitting Scale to measure levels of quiet quitting, and the single item burnout measure to measure job burnout. Moreover, a valid six-point Likert scale was used to measure turnover intention. All multivariable models were adjusted for the following confounders: gender, age, understaffed department, shift work, and work experience. The multivariable analysis identified a negative relationship between moral resilience and quiet quitting, job burnout, and turnover intention. In particular, we found that increased response to moral adversity and increased moral efficacy were associated with decreased detachment score, lack of initiative score, and lack of motivation score. Additionally, personal integrity was associated with reduced detachment score, while relational integrity was associated with reduced detachment score, and lack of initiative score. Moreover, response to moral adversity was associated with reduced job burnout. Also, increased levels of response to moral adversity were associated with lower probability of turnover intention. Moral resilience can be an essential protective factor against high levels of quiet quitting, job burnout, and turnover intention among nurses.
Background/Objectives: The nursing work environment, encompassing accessible resources and established processes, might affect nurses' professional behavior. Our aim was to examine the effect of nurse work environment on quiet quitting and work engagement among nurses. Methods: We performed a cross-sectional study with nurses in Greece. We used the “Practice Environment Scale-5” to measure nurse work environment, the “Quiet Quitting Scale” to measure quiet quitting, and the “Utrecht Work Engagement Scale-3” to measure work engagement among nurses. We developed multivariable regression models adjusted for gender, age, understaffed ward, shift work, and work experience. Results: After controlling for confounders, we found that lower nurse participation in hospital affairs, worse collegial nurse‐physician relationships, worse nursing foundations for quality of care, and lower levels of nurse manager ability, leadership, and support were associated with higher levels of quiet quitting among nurses. Moreover, our multivariable analysis identified a positive association between nurse manager ability, leadership, and support, collegial nurse‐physician relationships, nursing foundations for quality of care and work engagement among nurses. Conclusion: We found that poor nurses’ work environment was associated with higher levels of quiet quitting. Moreover, our findings showed that nurses work environment had a positive impact on work engagement.
AbstractOBJECTIVE To identify an optimal cut-off point for the TikTok Addiction Scale (TTAS). METHOD We performed a cross-sectional with a convenience sample. We collected our data in Greece during July 2024. We used a sample of TikTok users among the general population. We employed the Receiver Operating Characteristic analysis to identify an optimal cut-off point for the TTAS by using the Bergen Social Media Addiction Scale (BSMAS) and the Patient Health Questionnaire-4 (PHQ-4) as external criterions. We used the suggested cut-off points from the literature to develop dichotomous variables for BSMAS and PHQ-4. RESULTS We found a significant predictive power of TTAS for social media addiction, anxiety, and depression. We found that the best cut-off point for the TTAS is 3.23 (p-value < 0.001, Youden’s index = 0.72). In that case, the area under the curve was 0.91 (95% confidence interval = 0.86 - 0.97). Sensitivity and specificity of the TTAS were 0.76 and 0.96 respectively. Thus, mean TTAS score ≥3.23 suggested TikTok use disorder, while mean score from 1.00 to 3.22 suggested healthy users. The positive predictive value of the TTAS was 0.61, while the negative predictive value 0.98. CONCLUSIONS The best cut-off point for the TTAS was 3.23. TikTok users with mean TTAS score ≥3.23 should be further examined by mental health professionals. Further research should be conducted to validate our results.
