Comparison of the Association Between Presenteeism and Absenteeism among Replacement Workers and Paid Workers: Cross-sectional Studies and Machine Learning Techniques.

Background: Replacement drivers represent a significant portion of platform labor in the Republic of Korea, often facing night shifts and the demands of emotional labor. Research on replacement drivers is limited due to their widespread nature. This study examined the levels of presenteeism and absenteeism among replacement drivers in comparison to those of paid male workers in the Republic of Korea. Methods: This study collected data for replacement drivers and used data from the 6th Korean Working Conditions Survey for paid male workers over the age of 20 years. Propensity score matching was performed to balance the differences between paid workers and replacement drivers. Multivariable logistic regression was used to estimate the adjusted odds ratio (OR) and 95% confidence intervals for presenteeism and absenteeism by replacement drivers. Stratified analysis was conducted for age groups, educational levels, income levels, and working hours. The analysis was adjusted for variables including age, education, income, working hours, working days per week, and working duration. Results: Among the 1,417 participants, the prevalence of presenteeism and absenteeism among replacement drivers was 53.6% (n = 210) and 51.3% (n = 201), respectively. The association of presenteeism and absenteeism (adjusted OR [95% CI] = 8.42 [6.36-11.16] and 20.80 [95% CI = 14.60-29.62], respectively) with replacement drivers being significant, with a prominent association among the young age group, high educational, and medium income levels. Conclusion: The results demonstrated that replacement drivers were more significantly associated with presenteeism and absenteeism than paid workers. Further studies are necessary to establish a strategy to decrease the risk factors among replacement drivers.

The Association between New-Onset Depressive Symptoms and Participating in Medical Check-Ups among Elderly Individuals.

The association between adherence to medical check-ups and new-onset depressive symptoms, after adjusting for comprehensive risk factors such as social characteristics, remains unclear. This study aimed to assess the association between mental health and participating in medical check-ups. The survey data of participants aged 60 to 89 were recruited from the seventh Korean Longitudinal Study on Aging. The primary outcome was new-onset depressive symptoms within 2 years measured using the Center for Epidemiologic Studies Depression Scale. Participating in medical check-ups was defined as undergoing biennial medical check-ups. Multivariable logistic regression was performed to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) with consideration of a 2-year time lag. Among 4255 participants, the prevalence of new-onset depressive symptoms was 7.36% (n = 313). The prevalence of non-participation in medical check-ups was 11.96% (n = 509). The adjusted OR of new-onset depressive symptoms by non-participation in medical check-ups was 1.65 [95% CI 1.22-2.24; p = 0.001] after adjusting for various demographic, behavioral, occupational, and social participation characteristics. Our findings demonstrated a significant inverse relationship between participation in medical check-ups and new-onset depressive symptoms. It is necessary to monitor and manage depressive symptoms in vulnerable elderly individuals who do not participate in medical check-ups.

Resilience and Depressive Symptoms among Medical Staff in a Military Hospital Dedicated to the Treatment of COVID-19.

Coronavirus disease 2019 (COVID-19) is prevalent around the world, and many studies suggest that depression among medical staff is on the rise during the pandemic. This study aims to assess the relationship between depressive symptoms and individual resilience among military hospital personnel responsible for treating patients with COVID-19. Individuals from the Armed Forces Daejeon Hospital who responded to the questionnaires from 8 February to 15 February 2022 participated in this study. Resilience and depressive symptoms were measured via the Korean Resilience Quotient Test-53 and Patient Health Questionnaire-9, respectively. We employed multivariable logistic regression analysis to estimate Odds Ratios (ORs) and 95% Confidence Intervals (CIs) of depressive symptoms. Among 181 participants, the individuals with depressive symptoms and high resilience accounted for 8.8% and 61.9%, respectively. Significant correlations between depressive symptoms and both the low resilience and low resilience positivity groups were found (adjusted OR 10.30 [95% CI 1.74-61.01] and OR 13.90 [95% CI 1.93-100.02], respectively). This study notes a significant inverse relationship between depressive symptoms and resilience even after adjusting for demographic and occupational characteristics. To overcome depressive symptoms among hospital personnel, it is necessary to seek ways to improve individual resilience, especially positivity.

The Association between Replacement Drivers and Depressive Symptoms.

A replacement driver is a type of gig worker who provides driving services to the target point with the drunk driver's own car. This study aimed to examine the association of replacement drivers (ref: paid workers) with depressive symptoms. Information on replacement drivers was collected through online/offline surveys. Data from the 8th Korea National Health and Nutrition Examination Survey were applied to construct the control group. The Patient Health Questionnaire-9; ≥5 points was defined as depressive symptoms. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were calculated by performing multivariable logistic regression analysis. The mean age of replacement drivers was 56.11. The prevalence of depressive symptoms in replacement drivers and controls were 49.63% and 12.64%, respectively. Replacement drivers showed a higher association with depressive symptoms than paid workers (aOR 7.89, 95% CI [5.53-11.26]). This relationship was prominent in the older, low-education, and low-income groups. Linear discriminant analysis was the most effective in predicting depressive symptoms among the machine learning models. Using the replacement driver feature increased the AUC values of the models. Given the strong association between depressive symptoms and replacement drivers, in-depth studies to establish guidelines to prevent mental diseases among replacement drivers are required.