The COVID-19 pandemic significantly impacted healthcare workers in Europe, increasing workloads, mandating personal protective equipment (PPE) use, and enforcing strict safety measures. These factors led to heightened stress, emotional exhaustion, and mental health challenges. While the pandemic's effects were global, the response and adaptability varied across professions. Healthcare roles' inherent nature heavily influenced professional mental resilience, highlighting the need for adequate job resources to mitigate adverse circumstances. The pandemic exacerbated pre-existing issues such as burnout, mental fatigue, and resource scarcity among healthcare providers, necessitating extended working hours and adherence to stringent protocols. These factors contributed to stress and burnout, underscoring the need for a deeper understanding of the determinants of mental well-being within this crucial sector. The study uses Karasek's Job Demands-Control-Support (JDCS) model as a framework to investigate these determinants.

The JDCS model, drawing from the work of Cox and Howard (1990), Johnson and Hall (1988), and Karasek (1979), posits a causal link between job demands, control, social support, and worker well-being. The model highlights the interplay of these factors: high demands with high control can be empowering, low demands with high control can lead to underutilization, high demands with low control cause increased stress, and low demands with low control result in monotony and demotivation. The model's application to healthcare is particularly relevant, considering the sector's inherent demands, resource constraints, and interpersonal dynamics. Existing research emphasizes the impact of workload, social support, and work control on healthcare workers' well-being, highlighting the link between suboptimal care, violence towards patients, and the mental health of healthcare professionals. Emotional strain from resource scarcity is also a key concern, along with the prevalence of occupational stress and subsequent sickness leave. The literature also touches upon the impact of gender and age on healthcare workers' well-being, with women often facing more precarious employment and work-family conflicts, while older workers may experience ageism and difficulty adapting to new technologies.

This quantitative study utilizes data from Eurofound’s Living, Working, and COVID-19 survey, specifically focusing on a sample of 4626 healthcare workers across the European Union. Data collection spanned three rounds between April 2020 and March 2021. The study employs structural equation modeling (SEM) to test hypotheses based on the JDCS model. The key variables include job demands (workload, time pressure, emotional strain, isolation), job resources (PPE availability, supervisor support, peer support), and mental well-being (cheerfulness, calmness, activity, restfulness, interest in daily life). Covariance analysis (ANCOVA) was initially used to examine the relationship between job demands, job resources, and well-being while controlling for age and gender. Subsequently, SEM was applied to analyze the direct and indirect effects of job demands and job resources on mental well-being, testing the mediating and moderating roles of age and gender. The analysis considered the reliability of the constructs (Cronbach's alpha) and assessed the goodness of fit of the SEM models using various indices (NFI, RFI, IFI, TLI, RMSEA).

The ANCOVA analysis revealed significant associations (p<0.01) between mental well-being and several factors: job demands (positive association), availability of PPE (negative association), support from supervisors and peers (negative associations), and age (positive association). The SEM analysis confirmed the significant negative effect of job demands on mental well-being (β = -3.866, p<0.001) and the significant positive effect of job resources on mental well-being (β= -4.029, p=0.035). Job resources acted as a significant moderator, mitigating the negative impact of job demands on well-being (β = 1.753, p<0.001). Age was also identified as a significant predictor of well-being (β = 6.087, p<0.007) indicating that older workers generally reported higher levels of well-being while gender was also shown to be significant (β = 4.763, p<0.005). The model showed a good fit to the data. In detail, high job demands, such as feeling exhausted, drained, isolated, or lacking sufficient time or equipment, were strongly associated with lower mental well-being. Conversely, sufficient PPE, managerial support, and peer support were associated with higher levels of well-being. The model explained 83.9% of the variance in mental well-being.

The findings strongly support the JDCS model's applicability to understanding healthcare workers' mental well-being during the pandemic. The negative impact of high job demands on well-being is consistent with previous research, highlighting the urgency of addressing workload and work-related stress. The moderating role of job resources emphasizes the importance of providing adequate PPE, and fostering supportive interpersonal relationships within the workplace. The significant influence of age on well-being suggests potential age-related differences in coping mechanisms or resilience. The study acknowledges the limitations of generalizing results due to the feminized nature of the healthcare sector, and the inherent difficulty in testing gender and age as moderators within this specific context. Overall, the results highlight the importance of organizational interventions aimed at reducing job demands, enhancing job resources, and promoting a supportive work environment to safeguard the mental well-being of healthcare professionals.

This study provides robust evidence supporting the use of the JDCS model to understand the determinants of mental well-being among healthcare workers. The significant negative association between job demands and well-being, along with the mitigating effect of job resources, underscores the importance of workplace interventions focusing on reducing workload and improving access to support. Future research should investigate the specific mechanisms underlying the age effect and examine the nuances of gender differences more comprehensively in the healthcare sector. Further studies could explore interventions aimed at improving job resources, and strategies targeting specific age cohorts and gender dynamics within the healthcare workforce.

The study's reliance on self-reported data may introduce bias. The cross-sectional design limits the ability to establish causality definitively. The focus on the European context may limit the generalizability of findings to other settings. The sample, while large, may not perfectly represent the entire European healthcare workforce. Future studies could benefit from longitudinal designs, incorporating objective measures of job demands and resources, and broadening the geographical scope to increase generalizability.