There is renewed interest in person-centered approaches to understanding the structure of temperament. However, questions concerning temperament types are not frequently framed in a developmental context, especially during infancy. In addition, the most common person-centered techniques, Cluster Analysis (CA) and Latent Profile Analysis (LPA), have not been compared with respect to derived temperament types. To address these gaps, we set out to identify temperament types for younger and older infants, comparing LPA and CA techniques. Multiple data sets (N = 1,356; 672 girls, 677 boys) with maternal ratings of infant temperament obtained using the Infant Behavior Questionnaire-Revised (Gartstein & Rothbart, 2003) were combined. All infants were between 3 and 12 months of age (mean = 7.85; SD = 3.00). Due to rapid development in the first year of life, LPA and CA were performed separately for younger (n = 731; 3-to-8 months of age) and older (n = 625; 9-to-12 months of age) infants. Results supported 3-profile/cluster solutions as optimal for younger infants, and 5-profile/cluster solutions for the older subsample, indicating considerable differences between early/mid and late infancy. LPA and CA solutions produced relatively comparable types for younger and older infants. Results are discussed in the context of developmental changes unique to the end of the first year of life, which likely account for the present findings.
Early public health strategies to prevent the spread of COVID-19 in the United States relied on non-pharmaceutical interventions (NPIs) as vaccines and therapeutic treatments were not yet available. Implementation of NPIs, primarily social distancing and mask wearing, varied widely between communities within the US due to variable government mandates, as well as differences in attitudes and opinions. To understand the interplay of trust, risk perception, behavioral intention, and disease burden, we developed a survey instrument to study attitudes concerning COVID-19 and pandemic behavioral change in three states: Idaho, Texas, and Vermont. We designed our survey (n = 1034) to detect whether these relationships were significantly different in rural populations. The best fitting structural equation models show that trust indirectly affects protective pandemic behaviors via health and economic risk perception. We explore two different variations of this social cognitive model: the first assumes behavioral intention affects future disease burden while the second assumes that observed disease burden affects behavioral intention. In our models we include several exogenous variables to control for demographic and geographic effects. Notably, political ideology is the only exogenous variable which significantly affects all aspects of the social cognitive model (trust, risk perception, and behavioral intention). While there is a direct negative effect associated with rurality on disease burden, likely due to the protective effect of low population density in the early pandemic waves, we found a marginally significant, positive, indirect effect of rurality on disease burden via decreased trust (p = 0.095). This trust deficit creates additional vulnerabilities to COVID-19 in rural communities which also have reduced healthcare capacity. Increasing trust by methods such as in-group messaging could potentially remove some of the disparities inferred by our models and increase NPI effectiveness.
Addressed the ecology of deviant peer involvement, antisocial behavior and alcohol use, utilizing publically available information for indices of community risk/protective factors. A geospatial model was developed, combining geographic data (census, crime proximity, race/ethnicity, transportation accessibility) with information gathered for individual adolescents/household, geo-coded by home address. Adolescent-report of delinquency, association with deviant peers, substance use, and parental monitoring was obtained, along with parent-report of demographic characteristics. Deviant peer involvement was predicted by the Crime Proximity Index, with closeness of crime being associated with more deviant peer affiliation, as well as the Transportation Index, with greater accessibility leading to more involvement with troubled peers. Antisocial behaviors also increased with greater access to transportation. Adolescent alcohol use was lower in communities with a higher proportion of a non-Caucasian population, and increased with greater transportation access. Adolescent outcomes were associated with different prediction models, yet parental monitoring emerged as a consistent contributing factor.
Community crime exposure contributes to higher BMI as early as the preschool period, and blunted diurnal cortisol patterns may place children experiencing neighborhood adversity at greater risk for obesity.
Early public health strategies to prevent the spread of COVID-19 in the United States relied on non-pharmaceutical interventions (NPIs) as vaccines and therapeutic treatments were not yet available. Implementation of NPIs, primarily social distancing and mask wearing, varied widely between communities within the US due to variable government mandates, as well as differences in attitudes and opinions. To understand the interplay of trust, risk perception, behavioral intention, and disease burden, we developed a survey instrument to study attitudes concerning COVID-19 and pandemic behavioral change in three states: Idaho, Texas, and Vermont. We designed our survey (n = 1034) to detect whether these relationships were significantly different in rural populations. The best fitting structural equation models show that trust indirectly affects protective pandemic behaviors via health and economic risk perception. We explore two different variations of this social cognitive model: the first assumes behavioral intention affects future disease burden while the second assumes that observed disease burden affects behavioral intention. In our models we include several exogenous variables to control for demographic and geographic effects. Notably, political ideology is the only exogenous variable which significantly affects all aspects of the social cognitive model (trust, risk perception, and behavioral intention). While there is a direct negative effect associated with rurality on disease burden, likely due to the protective effect of low population density in the early pandemic waves, we found a marginally significant, positive, indirect effect of rurality on disease burden via decreased trust (p = 0.095). This trust deficit creates additional vulnerabilities to COVID-19 in rural communities which also have reduced healthcare capacity. Increasing trust by methods such as in-group messaging could potentially remove some of the disparities inferred by our models and increase NPI effectiveness.
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