BackgroundIdentifying modifiable factors associated with well-being is of increased interest for public policy guidance. Developments in record linkage make it possible to identify what contributes to well-being from a myriad of factors. To this end, we link two large-scale data resources; the Geoscience and Health Cohort Consortium, a collection of geo-data, and the Netherlands Twin Register, which holds population-based well-being data.ObjectiveWe perform an Environment-Wide Association Study (EnWAS), where we examine 139 neighbourhood-level environmental exposures in relation to well-being.MethodsFirst, we performed a generalized estimation equation regression (N = 11,975) to test for the effects of environmental exposures on well-being. Second, to account for multicollinearity amongst exposures, we performed principal component regression. Finally, using a genetically informative design, we examined whether environmental exposure is driven by genetic predisposition for well-being.ResultsWe identified 21 environmental factors that were associated with well-being in the domains: housing stock, income, core neighbourhood characteristics, livability, and socioeconomic status. Of these associations, socioeconomic status and safety are indicated as the most important factors to explain differences in well-being. No evidence of gene-environment correlation was found.SignificanceThese observed associations, especially neighbourhood safety, could be informative for policy makers and provide public policy guidance to improve well-being. Our results show that linking databases is a fruitful exercise to identify determinants of mental health that would remain unknown by a more unilateral approach.
Current understandings of similarity with media characters often focus on visible attributes including gender and race, yet overlook deep-level characteristics such as personality, attitudes, and experiences. In the present research, we address this limitation and develop and validate the Character Recognizability Scale (CRS), which captures different ways in which audiences can recognize themselves in characters. Based on a previous interview study, we formulated 26 scale items. Subsequently, we conducted two studies. In Study 1, we used a sample of 219 university students in the Netherlands to conduct an exploratory factor analysis. We determined the reliability, as well as criterion and convergent validity of the entire scale and the retained factors. In Study 2, we used a sample of 247 respondents in the United States to conduct a confirmatory factor analysis and replicate the results of the reliability and validity analyses. Based on Study 1, we kept 20 items. In both studies, the overall CRS scale as well as its subscales for Personality Recognizability (CRS-p), Attitudinal Recognizability (CRS-a), and Experiential Recognizability (CRS-e) showed a good internal consistency. They also showed criterion validity through an association with perceived similarity. Finally, the CRS and its subscales correlated positively with media engagement and exposure measures, thus demonstrating convergent validity.
Artificial intelligence-driven technology increasingly shapes work practices and, accordingly, employees’ opportunities for meaningful work (MW). In our paper, we identify five dimensions of MW: pursuing a purpose, social relationships, exercising skills and self-development, autonomy, self-esteem and recognition. Because MW is an important good, lacking opportunities for MW is a serious disadvantage. Therefore, we need to know to what extent employers have a duty to provide this good to their employees. We hold that employers have a duty of beneficence to design for opportunities for MW when implementing AI-technology in the workplace. We argue that this duty of beneficence is supported by the three major ethical theories, namely, Kantian ethics, consequentialism, and virtue ethics. We defend this duty against two objections, including the view that it is incompatible with the shareholder theory of the firm. We then employ the five dimensions of MW as our analytical lens to investigate how AI-based technological innovation in logistic warehouses has an impact, both positively and negatively, on MW, and illustrate that design for MW is feasible. We further support this practical feasibility with the help of insights from organizational psychology. We end by discussing how AI-based technology has an impact both on meaningful work (often seen as an aspirational goal) and decent work (generally seen as a matter of justice). Accordingly, ethical reflection on meaningful and decent work should become more integrated to do justice to how AI-technology inevitably shapes both simultaneously.
