Rebecca P. Ang

Background: Forensic risk and needs assessment interviews are commonly conducted for incarcerated sex offenders to inform treatment planning. Despite strong evidence showing the positive influence of rapport‐based investigative interviews on sex offenders' cooperation, disclosure and confession, the impact of rapport‐based interviews on treatment readiness has yet to be examined. Aims: As rapport features are mostly addressed in offender treatment and less in pre‐treatment assessments, this study aimed to examine the influence of perceived rapport features and personal factors on treatment readiness of incarcerated sex offenders after forensic risk and needs assessment interviews. We hypothesised that sex offenders' perceived rapport levels of the interviewers and personal factors significantly moderate positive change in their treatment readiness after the interviews. Methods: Incarcerated male sex offenders ( n = 80) participated in interviews conducted by trained correctional professionals ( n = 33). Treatment readiness ratings (pre‐ and post‐interview) and rapport features ratings (post‐interview) were collected from the sex offenders using pen and paper questionnaires. Personal data of interviewers and sex offenders were obtained from prison records and case files. Results: All ratings of rapport features (expertise, trust/respect, connected flow, cultural similarity, and attentiveness) and sex offending risk level (moderate and high) significantly moderated the change in treatment readiness of sex offenders after the interview. Conclusion: Rapport formed during forensic risk and needs assessment interviews can positively influence the treatment readiness of moderate and high risk sex offenders. Results suggest that correctional agencies may implement policies and training to apply rapport‐based interviewing techniques during forensic risk and needs assessment interviews to increase the likelihood of sex offenders starting and engaging in treatment.

Cyberbullying among adolescents is a serious concern, warranting the attention of both researchers and practitioners. While cyber-victimization is considered one of the most important factors leading to cyberbullying perpetration, more investigation is needed on the underlying mediating mechanisms, especially from a developmental perspective. Based on the social information processing model, this study investigated the direction of the relationship between cyber-victimization and cyberbullying perpetration as well as the underlying developmental mechanisms of moral disengagement. Parallel process latent growth curve modeling was used to jointly model the developmental trajectories of cyber-victimization and cyberbullying perpetration and to investigate the mediating role of moral disengagement. Three-wave longitudinal data—with a 1-year time interval between consecutive waves—were obtained from a sample of Chinese adolescents ( N = 1,422; 50.9% girls; mean age = 13.56) and analyzed. All participants completed self-report questionnaires on cyber-victimization, cyberbullying perpetration, and moral disengagement. Results showed that both the initial level and change in cyber-victimization predicted an increase in cyberbullying perpetration over time. Changes in moral disengagement mediated the relationship between initial levels of cyber-victimization and changes in cyber-victimization, and also changes in cyberbullying perpetration. Initial levels of cyber-victimization also indirectly influenced initial levels of cyberbullying perpetration through initial levels of moral disengagement. Furthermore, gender was significantly associated with cyber-victimization, with girls showing a lower likelihood of being cyberbullying victims than boys. By investigating the development of both cyberbullying victimization and perpetration among adolescents, this study provides valuable insights for future research and intervention programs aimed at protecting adolescents in the cyber world.

This article provides an overview of the articles in the special section on Parenting Across Cultures: Bridging Commonalities and Specificities in Parental Control and Warmth . To advance developmental science, we recommend using the cultural commonality and specificity framework in examining how the forms and functions of parenting behaviors might manifest similarly or differently across cultural context. This special section begins with a concept paper that introduces the commonality and specificity framework. This article highlights the importance of using established and validated theoretical frameworks in parenting science as a starting point, in the quest for establishing commonality in how parental control and parental warmth may look and function similarly across cultural contexts. At the same time, we recognize that against the backdrop of identifying generality, robust theoretical frameworks would and should allow sufficient room to accommodate specificities. These specificities would naturally arise due to differences in values and beliefs that families hold about desirable child socialization goals, and the ways through which these goals may be met. In this special section, we include studies that involve families with children from early childhood to adolescence residing in four societies—Indonesia, Singapore, Türkiye, and Chinese immigrants in the United States. These studies also employ diverse methods of inquiry, and their findings demonstrate the full utility of the cultural commonality and specificity framework in documenting and explaining where and how similarities and differences may occur.

Cyberbullying victimization and mental health symptoms are major concerns for children and adolescents worldwide. Despite the increasing number of longitudinal studies of cyberbullying and mental health among this demographic, the robustness of the causal associations between cyberbullying victimization and the magnitude of mental health symptoms remains unclear. This meta-analysis investigated the longitudinal impact of cyberbullying victimization on mental health symptoms among children and adolescents. A systematic search identified primary studies published in English between January 2010 and June 2021, yielding a sample of 27 studies encompassing 13,497 children and adolescents aged 8 to 19 years old. The longitudinal association between cyberbullying victimization and mental health symptoms among children and adolescents was found to be weakly positive and consistent across time and age. Three significant moderators were identified: the effect of cyberbullying victimization on mental health was larger among older children, groups with a higher proportion of males, and in more recent publications. No evidence of publication bias was detected. This study adds to the existing body of research by providing a new perspective on the long-term effects of cyberbullying victimization on the mental health of children and adolescents’ mental health. Furthermore, it underscores the necessity of developing effective cyberbullying prevention programs, interventions, and legal regulations to comprehensively address this issue.

Bullying has emerged as a critical social issue in Singapore, with many children and adolescents experiencing or witnessing bullying in school or online. Particularly noteworthy is the rise of cyberbullying as a significant concern for young people, driven by extensive technology use. This has led to an increased risk of cyberbullying victimization and perpetration among young people. This chapter presents a comprehensive review of the prevalence and patterns of both forms of bullying, shedding light on the social-ecological factors that may contribute to these behaviors. The analysis draws from studies conducted with samples from Singapore. Additionally, the chapter explores the consequences of school bullying and cyberbullying, considering both victimization and perpetration. It also scrutinizes the anti-bullying interventions and programs implemented in Singapore. The chapter concludes with a discussion of the implications for practical applications and suggestions for future research in this area.

There is an emerging, many-dimensional model of human functioning that has yet to be rigorously tested in adolescent psychopathology. The model is based, in part, on research suggesting stronger predictive power at the level of single items compared to the commonly used smaller number of higher-level constructs represented by scores or factors. Here, the model is tested in research relevant for the understanding how psychopathology relates to adolescent school enjoyment. We compared, explained, and clustered machine learning model results from a set of 99 self-reported items from different instruments that measured the behavioral and social-emotional problems of adolescents to predict school enjoyment. There is support for a many-dimensional model. Individual items had unique variances beyond noise that incrementally added out-of-sample predictive power above construct-level prediction, particularly for nonlinear machine learning classifiers. Explainable machine learning uncovered important predictors of low school enjoyment, and these were specific nuances of withdrawn/depressive behaviors, elevated fears and anxieties, lowered sensation-seeking, and some conduct problems—what we term risk nuances (cf. risk factors). Clustering further identified shared risk nuances among different groups of individuals with low school enjoyment. Our results suggest that item nuances are important in revealing many ways in which adolescents’ behavioral and social-emotional problems relate to school enjoyment at the individual and group levels. A many-dimensional model can complement current descriptive, predictive, and intervention efforts in adolescent psychopathology.

How well adolescents get along with others such as peers and teachers is an important aspect of adolescent development. Current research on adolescent relationship with peers and teachers is limited by classical methods that lack explicit test of predictive performance and cannot efficiently discover complex associations with potential non-linearity and higher-order interactions among a large set of predictors. Here, a transparently reported machine learning approach is utilized to overcome these limitations in concurrently predicting how well adolescents perceive themselves to get along with peers and teachers. The predictors were 99 items from four instruments examining internalizing and externalizing psychopathology, sensation-seeking, peer pressure, and parent-child conflict. The sample consisted of 3232 adolescents (M = 14.0 years, SD = 1.0 year, 49% female). Nonlinear machine learning classifiers predicted with high performance adolescent relationship with peers and teachers unlike classical methods. Using model explainability analyses at the item level, results identified influential predictors related to somatic complaints and attention problems that interacted in nonlinear ways with internalizing behaviors. In many cases, these intrapersonal predictors outcompeted in predictive power many interpersonal predictors. Overall, the results suggest the need to cast a much wider net of variables for understanding and predicting adolescent relationships, and highlight the power of a data-driven machine learning approach with implications on a predictive science of adolescence research.