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This study meta-analytically examined the effect of macro-level concentrated disadvantage on individual-level recidivism. Search results indicated research to date is designed to assess the incremental effect of concentrated disadvantage on recidivism above other risk factors. Using a multilevel random effects model, we found the estimated incremental effect of concentrated disadvantage was nonsignificant (log odds ratio = 0.03, p = .15, k = 48). However, effects varied by recidivism and offense type. Concentrated disadvantage does not add incremental utility when predicting general recidivism, but it does add incremental utility when assessing arrests (especially drug arrests) and violent reconvictions. Although individual-level risk factors and markers seem to explain most of the relationship between concentrated disadvantage and reoffending, concentrated disadvantage should not be summarily dismissed as irrelevant to recidivism. The overrepresentation of disadvantaged neighborhoods among the justice-involved—and the overrepresentation of the justice-involved in disadvantaged neighborhoods—requires further research on the disadvantage–recidivism relationship.

There is widespread public and academic interest in understanding the uses and effects of digital media. Scholars primarily use self-report measures of the quantity or duration of media use as proxies for more objective measures, but the validity of these self-reports remains unclear. Advancements in data collection techniques have produced a collection of studies indexing both self-reported and log-based measures. To assess the alignment between these measures, we conducted a pre-registered meta-analysis of this research. Based on 106 effect sizes, we found that self-reported media use correlates only moderately with logged measurements, that self-reports were rarely an accurate reflection of logged media use and that measures of problematic media use show an even weaker association with usage logs. These findings raise concerns about the validity of findings relying solely on self-reported measures of media use. In a pre-registered meta-analysis, Parry et al. find that, when self-reported media use is compared with digital logs of media use, subjective judgements are often inaccurate. This suggests caution when self-reports are used to test associations between media use and other outcomes.

Despite a plethora of research, the link between digital-technology use and psychological distress among young adults remains inconclusive. Findings in this area are typically undermined by methodological limitations related to measurement, study design, and statistical analysis. Addressing these limitations, we examined the prospective, within-persons associations between three aspects of objectively measured digital-technology use (duration and frequency of smartphone use, duration of social-media use) and three aspects of psychological distress (depression, anxiety, and social isolation) among a sample of young adults ( N = 384). Across 81 different model specifications, we found that most within-persons prospective effects between digital-technology use and psychological distress were statistically nonsignificant, and all were very small—even the largest effects were unlikely to register a meaningful impact on a person’s psychological distress. In post hoc subgroup analyses, we found scant evidence for the claim that digital-technology use is more harmful for women and/or younger people.

Background Research suggests that the disruptions introduced by the COVID-19 pandemic have led to increased psychological distress and time spent on digital technology among young people, thus intensifying pre-pandemic concerns regarding the putative effects of digital technology use on mental health. To robustly examine whether increases in digital technology use are associated with increases in psychological distress during the pandemic it is crucial to (1) collect objective data on digital technology use and (2) account for potential confounding caused by pandemic-related stressors. Methods We conducted a four-wave panel study of U.S. young adults (N=384; Mage = 24.5 ± 5.1; 57% female) from August-November of 2020. Participants provided screenshots of their iPhone “Screen Time” application and completed measures assessing current mental health status (depression, anxiety, and suicidal ideation) and pandemic-related impacts on well-being. We used random-intercept multilevel models to examine the within- and between-person associations between mental health, objective digital technology use, and pandemic-related stressors. Results Multilevel analyses revealed that none of the objectively-measured digital technology use variables were positively associated with depression, anxiety, or suicidal ideation at the within- or between-person levels. In contrast, pandemic-related impacts on mental health had by far the largest effects on depression, anxiety, and suicidal ideation. Limitations The convenience-based sample and use of single-item measures of pandemic-related impacts are limitations of the study. Conclusions Current speculations about the direct harms of digital technology use on mental health may be unfounded and risk diverting attention from a more likely cause: pandemic-related distress.

Supplemental material, sj-docx-1-cjb-10.1177_00938548221076094 for Recidivism in Context: A Meta-Analysis of Neighborhood Concentrated Disadvantage and Repeat Offending by Leah A. Jacobs, Laura Ellen Ashcraft, Craig J. R. Sewall, Danielle Wallace and Barbara L. Folb in Criminal Justice and Behavior

The association between depression and digital media use (DMU) has received substantial research and popular attention in recent years. While meta-analytic evidence indicates that there is a small, positive relationship between DMU and depression, almost all studies rely on self-report measures of DMU. Evidence suggests these measures are poor reflections of usage measures derived from digital trace data. Additionally, a recent study showed that the error in self-reported DMU is likely biased systematically by factors that are fundamental to the effect being investigated: respondents’ volume of use and level of depression. The present study harnesses cubic response surface analysis—a novel analytical approach in this domain—to advance our understanding of how inaccuracies in self-report measures of DMU can be explained by respondent attributes, in this case their level of depression and actual iPhone usage. A sample of 325 iPhone users provided estimates of their total iPhone use over the past week, their actual iPhone use as recorded by the Apple Screen Time application, and a measure of their depression. The results of the analysis indicate that depression is (a) more strongly associated with estimated than device-logged DMU; (b) more associated with overestimating than underestimating of DMU; and (c) more associated with inaccuracy at lower versus higher levels of DMU. The findings raise important questions concerning the validity of conclusions in this area and provide insight into the structure of measurement error in self-report estimates of DMU.

BackgroundYouth with bipolar disorder (BD) are at high risk for suicidal thoughts and behaviors and frequently experience interpersonal impairment, which is a risk factor for suicide. Yet, no study to date has examined the longitudinal associations between relationship quality in family/peer domains and suicidal thoughts and behaviors among youth with BD. Thus, we investigated how between-person differences - reflecting the average relationship quality across time - and within-person changes, reflecting recent fluctuations in relationship quality, act as distal and/or proximal risk factors for suicidal ideation (SI) and suicide attempts.MethodsWe used longitudinal data from the Course and Outcome of Bipolar Youth Study (N=413). Relationship quality variables were decomposed into stable (i.e., average) and varying (i.e., recent) components and entered, along with major clinical covariates, into separate Bayesian multilevel models predicting SI and suicide attempt. We also examined how the relationship quality effects interacted with age and sex.ResultsPoorer average relationship quality with parents (β=-.33, 95% Bayesian highest density interval (HDI) [-0.54, -0.11]) or friends (β=-.33, 95% HDI [-0.55, -0.11]) was longitudinally associated with increased risk of SI but not suicide attempt. Worsening recent relationship quality with parents (β=-.10, 95% HDI [-0.19, -0.03]) and, to a lesser extent, friends (β=-.06, 95% HDI [-0.15, 0.03]) was longitudinally associated with increased risk of SI, but only worsening recent relationship quality with parents was also associated with increased risk of suicide attempt (β=-.15, 95% HDI [-0.31, 0.01]). The effects of certain relationship quality variables were moderated by gender but not age.ConclusionsAmong youth with BD, having poorer average relationship quality with peers and/or parents represents a distal risk factor for SI but not suicide attempts. Additionally, worsening recent relationship quality with parents may be a time-sensitive indicator of increased risk for SI or suicide attempt.

Background Suicide is now the 2nd leading cause of death among adolescents and young adults. Social media’s influence on youth suicidal risk or attenuation of risk is a novel and rapidly expanding topic of research that requires attention from a broad range of mental health services professionals. We aimed to provide an updated review of social media-related risk and protective factors to youth deliberate-self harm (DSH) to guide mental health services professionals in offering care and support to youth vulnerable to suicide. Methods Studies on which primary research was conducted that evaluated young people’s use of social media platforms related to DSH were systematically searched via Scopus and identified through expert recommendation and the Association for Computing Machinery’s digital library of conference materials. The search focused on the timeframe June 2014 to September 2019, to offer an update since the time the most recent systematic reviews on this topic concluded their literatures searches. Quality was reviewed using the Mixed Methods Appraisal Tool (MMAT). Results A total of 38 articles were eligible, and 24 articles rated as high quality were included in a narrative review. Of the included articles, 19 explored DSH risk and 8 explored DSH protection. Most articles reported on cross-sectional quantitative or qualitative studies. Opportunities for both risks and benefits were explored, and potential influences of social media use were considered for subgroups of youth who may be especially vulnerable to suicide. Conclusions In the relatively short period of review, the association between social media use and youth DSH was tested in population-based studies, offering preliminary evidence for suicide prevention and treatment efforts. Further research is needed to understand the contribution of social media use to youth DSH, particularly among youth most vulnerable to suicidal risk.

Introduction Social media use is a risk factor for poor sleep among adolescents. It remains unclear whether social media use before bed impacts later sleep timing or whether youth turn to social media because of sleep problems, which impacts sleep timing. No study to date has examined this relationship using prospective designs and objective sleep measures among high-risk adolescents, who may be particularly vulnerable to social media use. Methods As a preliminary test of this relationship, 25 adolescents and young adults in an intensive outpatient program for depression and suicidality completed baseline measures of social media use and wore actiwatches for up to three months. Social media use included: 1) minutes of use within 2 hours of going to sleep, and 2) frequency of social media use due to difficulty falling or staying asleep. To examine social media as a predictor of sleep timing over the next month, actigraphic measures of sleep timing (i.e., onset) were used in the first month after baseline. Results Multilevel modeling indicated that higher levels of social media use in the 2 hours before bed (mean = 46.94 minutes) predicted later sleep timing (B = .02; SE = .01; p= .003). Similarly, higher frequency of social media use due to perceived sleep problems predicted later sleep timing (B = .22; SE = .11; p= .04). Models covaried for age, gender, and prior-day depressed mood. When simultaneously entered, only minutes of social media use in the 2 hours before bed significantly predicted later sleep timing. Conclusion Findings suggest that the relationship between social media use and sleep timing among adolescents with depression and suicidality may be driven by both social media use before bed and media use due to sleep problems. Research assessing objective social media use and daily relationships are needed to further disentangle this relationship. Support Jessica L Hamilton is supported by a T32 fellowship from NHLBI (HL082610; PI: Buysse). This research is supported by grants from the American Foundation for Suicide Prevention and University of Pittsburgh Clinical and Translational Science Institute.

OBJECTIVES: Examine how relationship quality in family and peer domains are associated with suicidal ideation (SI) in youth with Bipolar Disorder (BP). METHODS: 404 Course and Outcome of Bipolar Youth study participants were assessed for psychiatric disorders and SI at intake, and family/peer relationships the month after intake. Multivariate logistic regression examined associations between relationships and SI, controlling for significant covariates. RESULTS: 144 youth (36%) reported SI at intake; bivariate analyses indicated they had significantly worse family/peer relationships. Multivariate analyses showed family/peer relationships were associated with current SI, controlling for significant covariates. CONCLUSION: Results support associations between poor relationships and SI in BP youth, regardless of current mood symptom severity. Clinicians should assess relationships when completing risk assessments with BP youth.

The increasing ubiquity of mobile device and social media (SM) use has generated a substantial amount of research examining how these phenomena may impact public health. Prior studies have found that mobile device and SM use are associated with various aspects of well-being. However, a large portion of these studies relied upon self-reported estimates to measure amount of use, which can be inaccurate. Utilizing Apple’s “Screen Time” application to obtain actual iPhone and SM use data, the current study examined the accuracy of self-reported estimates, how inaccuracies bias relationships between use and well-being (depression, loneliness, and life satisfaction), and the degree to which inaccuracies were predicted by levels of well-being. Among a sample of 393 iPhone users, we found that: a.) participants misestimated their weekly overall iPhone and SM use by 22.1 and 16.6 hours, respectively; b.) the correlations between estimated use and well-being variables were consistently stronger than the correlations between actual use and well-being variables; and c.) the amount of inaccuracy in estimated use is associated with levels of participant well-being as well as amount of use. These findings suggest that estimates of device/SM use may be biased by factors that are fundamental to the relationships being investigated. **This manuscript is currently under review**

Existing research, policy information, and materials intended to teach children assume that certain behaviours will protect children in the event that a stranger tries to abduct them. However, there is little empirical basis for these assumptions. This article examines resistance strategies thought to reduce the likelihood that a child will be able to overcome an attempted stranger child abduction event. 78 cases of stranger child abduction that occurred in the United Kingdom between 1988 and 2014, including 25 attempted cases and 53 completed cases, were examined in order to ascertain the relative prevalence of various resistance types, and to assess whether the presence or absence of 7 key resistance strategies had an impact on the outcome of the abduction. Results show that direct, unequivocal verbal resistance, running away, and a composite approach where the victim runs away, calls for help and reports the offence were highly effective means of resisting an offender, whereas physical resistance, indirect verbal resistance and non-resistance were not effective. Female victims were almost twice as likely to employ any kind of resistance strategy against an offender as male victims. The implications of these findings for augmenting ways in which children are taught about safety are discussed.