Further mapping of the MMPI-3 onto HiTOP in a primary medical care and a college student sample.

The Hierarchical Taxonomy of Psychopathology (HiTOP; Kotov et al., 2017, 2021) is offered as a dimensional alternative to traditional categorical diagnostic nosologies such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD). HiTOP researchers have recently published an open-source assessment system for clinical implementation, the HiTOP Digital Assessment and Tracker (Jonas et al., 2021). Here, we argue that the Minnesota Multiphasic Personality Inventory-3 (MMPI-3; Ben-Porath & Tellegen 2020a), given its structural similarities to HiTOP, can augment these efforts to shift the diagnostic paradigm, with the additional strength of being comprehensively validated, standardized, and normed. Sellbom et al. (2021) examined the factor structure of the MMPI-3 Specific Problems Scales (plus RC6 and RC8), finding a pattern of latent factors much like those proposed by HiTOP in both a general mental health sample and a prisoner sample. The present study is a partial replication of Sellbom et al. (2021) with a primary medical care outpatient sample (n = 164) and a college student sample (n = 529). A sequential factoring approach yielded emergent structures that are comparable to the HiTOP model. These findings with different and important samples support the generalizability of the MMPI-3 in assessing HiTOP constructs. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

How local partisan context conditions prosocial behaviors: Mask wearing during COVID-19.

Does local partisan context influence the adoption of prosocial behavior? Using a nationwide survey of 60,000 adults and geographic data on over 180 million registered voters, we investigate whether neighborhood partisan composition affects a publicly observable and politicized behavior: wearing a mask. We find that Republicans are less likely to wear masks in public as the share of Republicans in their zip codes increases. Democratic mask wearing, however, is unaffected by local partisan context. Consequently, the partisan gap in mask wearing is largest in Republican neighborhoods, and less apparent in Democratic areas. These effects are distinct from other contextual effects such as variations in neighborhood race, income, or education. In contrast, partisan context has significantly reduced influence on unobservable public health recommendations like COVID-19 vaccination and no influence on nonpoliticized behaviors like flu vaccination, suggesting that differences in mask wearing reflect the publicly observable and politicized nature of the behavior instead of underlying differences in dispositions toward medical care.

Childhood cross-ethnic exposure predicts political behavior seven decades later: Evidence from linked administrative data.

Does contact across social groups influence sociopolitical behavior? This question is among the most studied in the social sciences with deep implications for the harmony of diverse societies. Yet, despite a voluminous body of scholarship, evidence around this question is limited to cross-sectional surveys that only measure short-term consequences of contact or to panel surveys with small samples covering short time periods. Using advances in machine learning that enable large-scale linkages across datasets, we examine the long-term determinants of sociopolitical behavior through an unprecedented individual-level analysis linking contemporary political records to the 1940 U.S. Census. These linked data allow us to measure the exact residential context of nearly every person in the United States in 1940 and, for men, connect this with the political behavior of those still alive over 70 years later. We find that, among white Americans, early-life exposure to black neighbors predicts Democratic partisanship over 70 years later.

The measurement of partisan sorting for 180 million voters.

Segregation across social groups is an enduring feature of nearly all human societies and is associated with numerous social maladies. In many countries, reports of growing geographic political polarization raise concerns about the stability of democratic governance. Here, using advances in spatial data computation, we measure individual partisan segregation by calculating the local residential segregation of every registered voter in the United States, creating a spatially weighted measure for more than 180 million individuals. With these data, we present evidence of extensive partisan segregation in the country. A large proportion of voters live with virtually no exposure to voters from the other party in their residential environment. Such high levels of partisan isolation can be found across a range of places and densities and are distinct from racial and ethnic segregation. Moreover, Democrats and Republicans living in the same city, or even the same neighbourhood, are segregated by party.