Predicting subjective well-being in a high-risk sample of Russian mental health app users

Association of family function and suicide risk in teenagers with a history of self-harm behaviors: mediating role of subjective wellbeing and depression

Background

A history of self-harm behaviors is closely associated with subsequent suicide death. Although many factors associated with suicide have been identified, it remains unclear how these factors interact to influence suicide risk, especially among teenagers with a history of self-harm behaviors.

Methods

Data were collected from 913 teenagers with a history of self-harm behaviors through a cross-sectional study. The Family Adaptation, Partnership, Growth, Affection, and Resolve index was used to assess teenagers' family function. The Patient Health Questionnaire-9 and the Generalized Anxiety Disorder-7 were used to evaluate depression and anxiety in teenagers and their parents, respectively. The Delighted Terrible Faces Scale was used to assess teenagers' perception of subjective wellbeing. The Suicidal Behaviors Questionnaire-Revised was used to evaluate teenagers' suicide risk. Student's t-test, one-way ANOVA, multivariate linear regression, Pearson's correlation, and a structural equation model (SEM) were applied to data analysis.

Results

Overall, 78.6% of teenagers with a history of self-harm behaviors were at risk for possible suicide. Female gender, severity of teenagers' depression, family function, and subjective wellbeing were significantly associated with suicide risk. The results of SEM suggested that there was a significant chain mediation effect of subjective wellbeing and depression between family function and suicide risk.

Conclusion

Family function was closely associated with suicide risk in teenagers with a history of self-harm behaviors, and depression and subjective wellbeing were sequential mediators in the association between family function and suicide risk.

Factors Affecting the Use of Social Networks and Its Effect on Anxiety and Depression among Parents and Their Children: Predictors Using ML, SEM and Extended TAM

Previous research has found support for depression and anxiety associated with social networks. However, little research has explored parents' depression and anxiety constructs as mediators that may account for children's depression and anxiety. The purpose of this paper is to test the influence of different factors on children's depression and anxiety, extending from parents' anxiety and depression in Jordan. The authors recruited 857 parents to complete relevant web survey measures with constructs and items and a model based on different research models TAM and extended with trust, analyzed using SEM, CFA with SPSS and AMOS, and ML methods, using the triangulation method to validate the results and help predict future applications. The authors found support for the structural model whereby behavioral intention to use social media influences the parent's anxiety and depression which correlate to their offspring's anxiety and depression. Behavioral intention to use social media can be enticed by enjoyment, trust, ease of use, usefulness, and social influences. This study is unique in exploring rumination in the context of the relationship between parent-child anxiety and depression due to the use of social networks.

Predicting Intrinsic and Extrinsic Goal Contents Pursuit on Social Media

Goal contents pursuit reflects the motivational personality and can be an excellent indicator to predict individuals' life satisfaction and daily behaviors. However, due to the expense and subjective bias of questionnaires, it is challenging to obtain individual data and explore the effects of goal contents pursuit in conventional studies. Social media provides individuals with a communication context that can be used as a proxy to infer personality based on a massive of media footprints information. This study obtained 456 Weibo active users' self-reports of goal contents pursuit scale and their online behaviors that is established to train a competent machine learning model, which then successfully identifies the classification of intrinsic and extrinsic goals. From the perspective of Weibo users' features (i.e., basic, interactive, linguistic, and emotional features), the systematic comparison shows the significant differences in the pursuit level of intrinsic and extrinsic goals. This study advances the methodology of employing machine learning and online data to objectively delineate individual goal contents pursuit and paves the way to explore a massive number of individuals' personalities and behaviors.