The processes linking job characteristics to school performance and satisfaction in a sample of 253 full-time college students were examined from 2 role theory perspectives, 1 of which emphasized resource scarcity and the other resource expansion. Model tests using structural equation modeling showed that 2 resource-enriching job characteristics, job-school congruence and job control, were positively related to work-school facilitation (WSF). Two resource-depleting job characteristics, job demands and work hours, were positively related to work-school conflict (WSC), and job control was negatively related to WSC. In turn, WSF was positively related to school performance and satisfaction, and WSC was negatively related to school performance. Both WSF and WSC mediated the relationship between the job characteristics and school outcomes. There was no evidence of interactive effects between enriching and depleting job characteristics on interrole processes.

This study used objective measures of job characteristics appended to the National Survey of Midlife Development in the United States (MIDUS), self-reported job characteristics, and an individual resource characteristic (orientation toward personal growth) to test a theory of workfamily facilitation. Results indicated that resource-rich jobs enable work-to-family facilitation. A higher level of work-to-family facilitation was reported by individuals in jobs with more autonomy and variety and whose jobs required greater substantive complexity and social skill. There was no support for the hypotheses that these effects would be more pronounced for individuals with higher levels of personal growth. The authors found significant differences in the strength of the associations of job characteristics with work-to-family facilitation and work-tofamily conflict, suggesting they are different constructs with distinct antecedents.

Workers bear a heavy share of the burden of how countries contend with COVID-19; they face numerous serious threats to their occupational health ranging from those associated with direct exposure to the virus to those reflecting the conflicts between work and family demands. Ten experts were invited to comment on occupational health issues unique to their areas of expertise. The topics include work-family issues, occupational health issues faced by emergency medical personnel, the transition to telework, discrimination against Asian-Americans, work stressors, presenteeism, the need for supportive supervision, safety concerns, economic stressors, and reminders of death at work. Their comments describe the nature of the occupational health concerns created by COVID-19 and discuss both unanswered research questions and recommendations to help organizations reduce the impacts of COVID-19 on workers.

Using personal digital assistants, 91 parents employed in non-professional occupations were surveyed for 14 consecutive days about their job characteristics and work-family experiences. We found significant daily variation in work-to-family conflict (WFC) and work-to-family facilitation (WFF) that was predictable from daily job characteristics. Higher levels of WFC were associated with greater job demands and control at work. Contrary to the demands-control model (Karasek, 1979), these two job characteristics interacted such that the relationship between demands and WFC was stronger when control was high. We also found that demands were negatively related and control and skill level positively related to WFF. The results suggest ways in which jobs may be redesigned to enhance individuals' work-family experiences.

Summary Model selection is difficult. Even in the apparently straightforward case of choosing between standard linear regression models, there does not yet appear to be consensus in the statistical ecology literature as to the right approach. We review recent works on model selection in ecology and subsequently focus on one aspect in particular: the use of the Akaike Information Criterion (AIC) or its small‐sample equivalent, AICC. We create a novel framework for simulation studies and use this to study model selection from simulated data sets with a range of properties, which differ in terms of degree of unobserved heterogeneity. We use the results of the simulation study to suggest an approach for model selection based on ideas from information criteria but requiring simulation. We find that the relative predictive performance of model selection by different information criteria is heavily dependent on the degree of unobserved heterogeneity between data sets. When heterogeneity is small, AIC or AICC are likely to perform well, but if heterogeneity is large, the Bayesian Information Criterion (BIC) will often perform better, due to the stronger penalty afforded. Our conclusion is that the choice of information criterion (or more broadly, the strength of likelihood penalty) should ideally be based upon hypothesized (or estimated from previous data) properties of the population of data sets from which a given data set could have arisen. Relying on a single form of information criterion is unlikely to be universally successful.