A multilevel analysis of the role personality play between work organization conditions and psychological distress

Subjective dry eye symptoms and associated factors among the national general population in China during the COVID-19 pandemic: A network analysis

Background

The coronavirus disease 2019 (COVID-19) pandemic has presented numerous challenges to public health, including the emergence of subjective dry eye symptoms among individuals. However, there is currently a dearth of comprehensive knowledge regarding the associated factors with subjective dry eye symptoms in the general population.

Methods

A nationwide survey in China was carried out from 20 June to 31 August 2022, involving 21 916 participants. Subjective dry eye symptoms were assessed using the Ocular Surface Disease Index-6. We employed random forest classification to select potential associated factors based on the socioecological model. We also conducted univariate and multivariable logistic regression analyses to explore the factors associated with subjective dry eye symptoms. Network analyses were employed to explore the network structure of subjective dry eye symptoms and associated factors.

Results

The item "watching TV (or similar task)" displayed the highest node strength and exhibited the highest predictability within the network. The results of the multivariate logistic regression analysis demonstrated significant associations between subjective dry eye symptoms and several factors, including loneliness (odds ratio (OR) = 1.29; 95% confidence interval (CI) = 1.26 to 1.32), problematic Internet use (OR = 1.08; 95% CI = 1.08 to 1.09), family communication (OR = 1.01; 95% CI = 1.00 to 1.01), the presence of depression (OR = 1.53; 95% CI = 1.39 to 1.68) and anxiety (OR = 1.31; 95% CI = 1.17 to 1.47) symptoms, older age (OR = 1.01; 95% CI = 1.01 to 1.01), self-rated health status (OR = 0.99; 95% CI = 0.98 to 0.99), family health (OR = 0.97; 95% CI = 0.96 to 0.97), health literacy (OR = 0.98; 95% CI = 0.97 to 0.99) and the agreeableness personality trait (OR = 0.97; 95% CI = 0.95 to 0.99).

Conclusions

These findings have important implications for public health interventions targeting the prevention and management of subjective dry eye symptoms in the general population. Strategies addressing individual risk factors and supporting psychological well-being may prove beneficial in mitigating the burden associated with subjective dry eye symptoms.

Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic

Objective

The COVID-19 pandemic has become a major public health concern over the past 3 years, leading to adverse effects on front-line healthcare workers. This study aimed to develop a Body Mass Index (BMI) change prediction model among doctors and nurses in North China during the COVID-19 pandemic, and further identified the predicting effects of lifestyles, sleep quality, work-related conditions, and personality traits on BMI change.

Methods

The present study was a cross-sectional study conducted in North China, during May-August 2022. A total of 5,400 doctors and nurses were randomly recruited from 39 COVID-19 designated hospitals and 5,271 participants provided valid responses. Participants' data related to social-demographics, dietary behavior, lifestyle, sleep, personality, and work-related conflicts were collected with questionnaires. Deep Neural Network (DNN) was applied to develop a BMI change prediction model among doctors and nurses during the COVID-19 pandemic.

Results

Of participants, only 2,216 (42.0%) individuals kept a stable BMI. Results showed that personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions had effects on the BMI change among doctors and nurses. The prediction model for BMI change was developed with a 33-26-20-1 network framework. The DNN model achieved high prediction efficacy, and values of R ², MAE, MSE, and RMSE for the model were 0.940, 0.027, 0.002, and 0.038, respectively. Among doctors and nurses, the top five predictors in the BMI change prediction model were unbalanced nutritional diet, poor sleep quality, work-family conflict, lack of exercise, and soft drinks consumption.

Conclusion

During the COVID-19 pandemic, BMI change was highly prevalent among doctors and nurses in North China. Machine learning models can provide an automated identification mechanism for the prediction of BMI change. Personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions have contributed to the BMI change prediction. Integrated treatment measures should be taken in the management of weight and BMI by policymakers, hospital administrators, and healthcare workers.

Work-related, socio-cultural, and personal factors associated with locus of control among Australian general practitioners

BACKGROUND

Locus of control (LoC) is a social cognition, that relates to the level of self-control that people have over their personal environment that influences their life. In this context, LoC is frequently associated with work-related behavioral outcomes, ranging from job attitudes, career behaviors, stress, and burnout.

OBJECTIVE

To investigate the association between LoC, and work-related behavioral factors, socio-cultural factors, and personal factors among Australian General Practitioners (GPs).

METHODS

This study utilized data from the 2010 Medicine in Australia: Balancing Employment and Life (MABEL) survey of doctors. Locus of control (LoC) was measured by a 7-point Likert scale based on Pearlin-Schooler Mastery/Self-efficacy 7-item Scale. Higher score indicated lower internal LoC. Multivariate linear regression model was performed to determine the independent predictors of LoC.

RESULTS

Of 3,664 GP participants, LoC did not differ by gender. Poor/fair self-rated health, working in urban location, running a stressful practice, poor balance of professional and personal commitments, poor support network, financial circumstances after retirement, and perception of unrealistic expectation by patients were significant predictors for a lower Internal LoC in a multivariate linear regression model. Adjusted R² explained 22.4% of variation in predicting the LoC in our models.

CONCLUSIONS

LoC of Australian GPs is negatively affected by poor work-life balance, inadequate support, and unrealistic patient expectation. These work-place specific factors could be targeted by interventions to improve GPs wellbeing.

Teleworking, Work Engagement, and Intention to Quit during the COVID-19 Pandemic: Same Storm, Different Boats?

The ability to retain and engage employees is now, more than ever, a major strategic issue for organizations in the context of a pandemic paired with a persistent labor shortage. To this end, teleworking is among the work organization conditions that merit consideration. The purpose of this cross-sectional study is to examine the direct and indirect effects of teleworking on work engagement and intention to quit, as well as the potential moderating effect of organizational and individual characteristics on the relationship between teleworking, work engagement, and intention to quit during the COVID-19 pandemic, based on a sample of 254 Canadian employees from 18 small and medium organizations. To address these objectives, path analyses were conducted. Overall, we found that teleworking, use of emotion, skill utilization, and recognition appear to be key considerations for organizations that wish to increase work engagement and decrease intention to quit, in the context of a pandemic paired with a labor shortage. Our results extend the literature by revealing the pathways through which teleworking, use of emotion, skill utilization, and recognition are linked to work engagement and intention to quit, and by suggesting specific interventions and formation plans that are needed.