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Purpose Most research on the work conditions and family responsibilities associated with work-family conflict and other measures of mental health uses the individual employee as the unit of analysis. We argue that work conditions are both individual psychosocial assessments and objective characteristics of the proximal work environment, necessitating multilevel analyses of both individual- and team-level work conditions on mental health. Methodology/approach This study uses multilevel data on 748 high-tech professionals in 120 teams to investigate relationships between team- and individual-level job conditions, work-family conflict, and four mental health outcomes (job satisfaction, emotional exhaustion, perceived stress, and psychological distress). Findings We find that work-to-family conflict is socially patterned across teams, as are job satisfaction and emotional exhaustion. Team-level job conditions predict team-level outcomes, while individuals’ perceptions of their job conditions are better predictors of individuals’ work-to-family conflict and mental health. Work-to-family conflict operates as a partial mediator between job demands and mental health outcomes. Practical implications Our findings suggest that organizational leaders concerned about presenteeism, sickness absences, and productivity would do well to focus on changing job conditions in ways that reduce job demands and work-to-family conflict in order to promote employees’ mental health. Originality/value of the chapter We show that both work-to-family conflict and job conditions can be fruitfully framed as team characteristics, shared appraisals held in common by team members. This challenges the framing of work-to-family conflict as a “private trouble” and provides support for work-to-family conflict as a structural mismatch grounded in the social and temporal organization of work.

Background: Health systems are increasingly recognizing the importance of collecting social determinants of health (SDoH) data. However, gaps remain in our understanding of facilitators or barriers to collection. To address these gaps, we evaluated a real-world implementation of a SDoH screening tool. Methods: We conducted a retrospective analysis of the implementation of the SDoH screening tool at Mayo Clinic in 2019. The outcomes are: (1) completion of screening and (2) the modality used (MyChart: filled out on patient portal; WelcomeTablet: filled out by patient on a PC-tablet; EpicCare: data obtained directly by provider and entered in chart). We conducted logistic regression for completion and multinomial logistic regression for modality. The factors of interest included race and ethnicity, use of an interpreter, and whether the visit was for primary care. Results: Overall, 58.7% (293,668/499,931) of screenings were completed. Patients using interpreters and racial/ethnic minorities were less likely to complete the screening. Primary care visits were associated with an increase in completion compared with specialty care visits. Patients who used an interpreter, racial and ethnic minorities, and primary care visits were all associated with greater WelcomeTablet and lower MyChart use. Conclusion: Patient and system-level factors were associated with completion and modality. The lower completion and greater WelcomeTablet use among patients who use interpreters and racial and ethnic minorities points to the need to improve screening in these groups and that the availability of the WelcomeTablet may have prevented greater differences. The higher completion in primary care visits may mean more outreach is needed for specialists.