The determinants of mental well-being of healthcare professionals during the COVID-19 pandemic

The study investigates how job demands, control, and social support shape the mental well-being of healthcare workers during the COVID-19 pandemic, using the Job Demand-Control-Support (JDCS) model as the theoretical framework. The pandemic imposed heightened workloads, mandatory PPE use, and strict safety protocols, contributing to stress, emotional exhaustion, and mental strain among health professionals. The JDCS model posits that well-being results from the interplay of job demands (e.g., workload, time pressure, emotional strain), job control (decision latitude and autonomy), and social support (from supervisors, peers, and organizations). The research formulates and tests hypotheses that higher job demands adversely relate to mental well-being, while job resources directly enhance well-being and moderate the negative effects of demands. It also explores whether gender and age moderate these relationships in the pandemic context.

Prior work in healthcare consistently links high workload and limited control to stress, fatigue, burnout, and poorer mental health, with social support mitigating these effects. The JDCS and related models (Karasek; Johnson & Hall; Cox & Howard; Theorell) frame the psychosocial mechanisms connecting demands, control, and support to health outcomes. Organizational factors such as resource sufficiency (personnel, equipment), supportive leadership, and collegial support are pivotal for worker well-being and care quality. Evidence highlights risks of absenteeism, turnover, depression, and even suicide in poor work environments. Research on aging suggests older workers often show resilience and adaptability, though they may face discrimination; younger workers may adapt more readily to technological change. Gendered divisions of labor and precarious conditions disproportionately affect women’s mental health; workplace and social supports can buffer work-family conflicts. Within healthcare’s constrained autonomy and protocolization, training and support improve satisfaction and retention. This body of literature motivates examining demands, control/resources, and support as determinants of well-being during COVID-19.

Data: Eurofound’s Living, Working and COVID-19 survey, online across EU-27, using rounds: Round 1 (Apr 9–Jun 11, 2020), Round 2 (Jun–Jul 27, 2020), and Round 3 (Feb–Mar 2021). The broader survey included 34,571 employees; the health sector subsample under analysis comprised several thousand healthcare workers (abstract notes n=4,626; analytical sample sizes vary due to missing data, with 4,583 records referenced in models). Composition: 31.7% men, 67.3% women; mean age 46.4. During the pandemic, average weekly hours caring for children ≈10.5 and for other dependents ≈3.15. Perceived occupational risk indicators: mean perceived contagion risk 0.62 (0=no risk, 1=high risk); physical contact 1.62 (1=always, 5=never); information on prevention 1.35 (1=very well informed, higher=worse); PPE requirement 0.79 (0=no, 1=yes). Measures: - Job Demands (JD) from six items (JD1 useful work; JD2 physically exhausted; JD3 emotionally drained; JD4 isolated; JD5 enough time; JD6 adequate equipment at home). - Job Resources (JR) from three items: JR1 PPE provision; JR2 manager support; JR3 colleague/peer support. - Mental Well-Being (MWB) from five items (MWB1 cheerful/good spirits; MWB2 calm/relaxed; MWB3 active/vigorous; MWB4 fresh/rested on waking; MWB5 life filled with interesting things). Analysis: - Co-ANOVA to assess associations between JD, JR components, age, gender, and composite well-being (WB). - Structural Equation Modeling (SEM) specifying latent constructs JD, JR, and WB, with direct and indirect paths to test hypotheses and moderation via indirect effects. Reliability: Cronbach’s alpha JD=0.648, JR=0.582, WB=0.897. Model fit indices: NFI=0.994, RFI=0.985, IFI=0.996, TLI=0.992, RMSEA=0.017, with WB equation R²=0.839. Pagan test p=0.655 supported model assumptions for covariance analysis.

- Co-ANOVA showed significant marginal effects of age, gender (p=0.015), job demands, and job resources components on well-being; tested interactions were not significant and omitted. Age had a positive effect (coef ≈0.07), job demands had a notable effect (coef ≈0.11), and men showed a deviation of ≈0.189 versus women. - Well-being means: men ≈3.922, women ≈3.711. A separate Well-being Index reported mean ≈514 (SD ≈21.36). - SEM path estimates (all p<0.05): WB from JD (estimate ≈−3.866; p<0.001), WB from JR (estimate ≈−4.029; p=0.035), and JD from JR (estimate ≈1.753; p<0.001). Measurement loadings for MWB items ranged ≈0.764–0.811; JR loadings strongest for support from managers (≈0.713) and colleagues (≈0.772), and for PPE (reference). JD indicators for exhaustion and emotional drain loaded strongly. - Fit indices indicated excellent fit (NFI 0.994; IFI 0.996; TLI 0.992; RMSEA 0.017). - Hypotheses supported: H1 (higher job demands associated with poorer mental well-being); H2 (job resources significant predictors of well-being, including PPE availability and support); H3 (resources relate to demands and act to mitigate their impact); H4 and H5: Co-ANOVA indicates gender (p≈0.015) and age (p<0.01) are influential covariates. - From the abstract and SEM tables: significant associations were observed for physical/mental work demands (p<0.001), PPE availability (p=0.035), supervisor support (p<0.001), peer support (p<0.007), and age (p<0.007).

Findings validate the JDCS model in a large EU healthcare sample during COVID-19. Elevated demands (physical and emotional strain, exhaustion, isolation, time pressure) are strongly linked to reduced mental well-being, supporting H1. Job resources—particularly PPE provision and social support from managers and peers—are significant and act to bolster well-being and temper demand-related strain (supporting H2 and H3). Age and gender show associations with well-being in covariate analyses, aligning with literature on differential vulnerabilities and resilience (H4/H5 context). Results dovetail with prior studies documenting pandemic-related stressors driving burnout, anxiety, and depression, while organizational and social supports buffer adverse outcomes. The strong SEM fit and high explained variance (R²≈0.84 for well-being) underscore the model’s explanatory power. Practically, adequate resources and supportive climates appear essential in crisis periods to sustain workforce mental health and service quality.

The study demonstrates that the JDCS framework effectively explains mental well-being among European healthcare workers during COVID-19: higher job demands are detrimental, while resources (PPE, managerial and peer support) directly enhance and help buffer well-being. Gender and age are relevant covariates in well-being variability. Practical implications include expanding worker autonomy and control, cultivating supportive leadership and collegial networks, ensuring adequate PPE and safety resources, and investing in training, professional development, and clear communication channels. These measures can improve worker well-being, patient care quality, and organizational resilience against future crises. Future research should extend across roles (e.g., physicians, managers), contexts, and cultures, and incorporate more objective measures to complement self-reports, enabling stronger causal inference and generalizability.

- Self-reported measures of JDCS constructs may introduce bias; limited control over confounding variables. - Study focuses on the European healthcare sector during COVID-19; findings may not generalize to other cultural or occupational contexts. - Some inconsistencies and missing data reduce sample size in multivariable models. - The cross-sectional nature of survey rounds constrains causal interpretation; more objective indicators and longitudinal designs are recommended. - Future work should include varied healthcare roles and settings, and triangulate self-reports with administrative or behavioral data.