Notes

Digital non-technical capabilities examination education is an excellent, scalable way of novice raters.
The present study examined the associations of positive, negative, and disorganized schizotypy dimensions assessed by the Multidimensional Schizotypy Scale with 5 interview-rated personality disorder diagnoses and traits in 151 young adults. see more As hypothesized, all 3 schizotypy dimensions were associated with impaired functioning. Positive schizotypy was associated with schizotypal and borderline personality traits; negative schizotypy was associated with schizotypal, schizoid, paranoid, and avoidant personality traits; and disorganized schizotypy was associated with paranoid, borderline, and avoidant personality disorder traits. Negative schizotypy predicted broad diagnoses of Cluster A personality disorders. Both negative and disorganized schizotypy predicted the broad diagnosis of any of the 5 personality disorders. The study further examined the association of the schizotypy dimensions with the individual personality disorder criteria to better understand the overall associations. Given the common origins and high comorbidity, we examined whether the schizotypy dimensions explained the association of borderline and schizotypal personality disorder traits. Positive and disorganized schizotypy fully mediated the association between borderline and schizotypal traits. In summary, the study replicated and extended previous findings that the schizotypy dimensions are uniquely associated with personality disorders and traits, as well as impaired functioning, in young adults. The study also provided additional support for the construct validity of the Multidimensional Schizotypy Scale and for the use of psychometric assessment of schizotypy. (PsycInfo Database Record (c) 2021 APA, all rights reserved).Children with a constellation of dark traits may be particularly challenging to parents because these traits are associated with an increased chance for parents to lose a supportive attitude in dealing with the child's difficultness and to turn instead toward punishing strategies. The present study looks with more detail into the construct of parental punishment and examines differences and similarities in the effects of physical (harsh) versus nonphysical (corrective) discipline on the developmental course of childhood five-factor model-based dark traits across a 10-year time span. Data were drawn from an ongoing (masked for review) longitudinal study, including five assessment points across 10 years (Ntime 1 = 720, 54.4% girls, age range Time 1 = 8-14.78 years, M = 10.73, SD = 1.39). Latent growth modeling suggested significant differences between both kinds of parental discipline in terms of contrasting effects on subsequent growth in dark traits and also showed a number of age-and gender-specific effects of parental discipline on the developmental course of dark traits. These findings underscore the relevance of a more differentiated perspective on effects of parental punishment in understanding childhood maladaptive trait outcomes and may offer fruitful guidelines for the development of intervention programs targeting children that are difficult to manage. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Health-related norms in social networks can influence whether people are open to health behavior change. Yet, little is known about how social networks relate to the ways individual brains respond to persuasive health messaging. The current study focuses on ventromedial prefrontal cortex (VMPFC) activity as an index of neural receptivity to health messages that may be related to behavior change. The study tested whether health-related norms and perceived physical activity levels within participants' social networks are associated with neural receptivity to health messages.

Adults who initially reported under 200 minutes/week of physical activity (N = 146) rated the perceived physical activity levels of, and closeness to, each person in their core social network. VMPFC activity was monitored using fMRI while participants viewed persuasive health messages promoting physical activity. Longitudinal changes in sedentary behavior were objectively logged using wrist-worn accelerometers throughout a 2-week baselinfo Database Record (c) 2021 APA, all rights reserved).
The sleep of individuals who provide unpaid care for children with medical needs is likely to be significantly impacted by this role. Sleep may be affected by the practical tasks undertaken during the night (e.g., administering medication), in addition to the emotional impact (e.g., worry, rumination). The aim of this systematic review was to examine the available literature on the impact of caregiving for children with medical needs on caregivers' sleep.

Electronic databases, including PubMed, Medline, and Web of Science, were searched using predetermined criteria. Studies were included if they used validated subjective or objective measures of caregiver sleep, in contexts where caregivers were providing care for one or more children with medical needs. Data on study population, research design, and outcome measures were extracted, and study quality was reviewed by two authors.

Search criteria produced 2,172 studies for screening. Based on inclusion criteria, 40 studies were included in the final review. Sleep of caregivers of children with medical needs was poorer than that for noncaregivers. Poor sleep included reduced sleep duration, impaired sleep efficiency, increased wake after sleep onset, and perceived poorer sleep quality.

Providing unpaid care for children with medical needs is associated with sleep disturbances, including less total sleep, and poorer sleep quality. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Providing unpaid care for children with medical needs is associated with sleep disturbances, including less total sleep, and poorer sleep quality. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
The prospective relationships between poor sleep health, poor diet quality, and physical inactivity with self-rated health (SRH) are not well described. The aim of this study was to assess individual and joint associations between high-risk health behaviors and incident poor SRH.

Participants from the Household Income and Labor Dynamics in Australia longitudinal cohort reporting "good" SRH in 2013 were included (n = 8,853) in 2020 data analysis. Logistic regression was used to assess odds of poor SRH in 2017 associated with (a) individual, (b) count, and (c) unique combinations of high-risk behaviors reported in 2013.

In the sample (48% female, M
= 45.2 years, SD = 16.8), poor sleep health (OR = 1.66, 95% CI [1.38, 2.01]), physical inactivity (OR = 1.18, [1.01, 1.38]), and poor diet quality (OR = 1.38, [1.16, 1.65]) were associated with increased odds of poor SRH. Reporting one (OR = 1.76, [1.27, 2.43]), two (OR = 2.16, [1.57, 2.98]), and three (OR = 2.99, [2.02, 4.41]) high-risk behaviors was associated with increased odds of poor SRH.
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