Treating conduct disorder: An effectiveness and natural language analysis study of a new family-centred intervention program

Predicting negative attitudes towards suicide in social media texts: prediction model development and validation study

Background

Implementing machine learning prediction of negative attitudes towards suicide may improve health outcomes. However, in previous studies, varied forms of negative attitudes were not adequately considered, and developed models lacked rigorous external validation. By analyzing a large-scale social media dataset (Sina Weibo), this paper aims to fully cover varied forms of negative attitudes and develop a classification model for predicting negative attitudes as a whole, and then to externally validate its performance on population and individual levels.

Methods

938,866 Weibo posts with relevant keywords were downloaded, including 737,849 posts updated between 2009 and 2014 (2009-2014 dataset), and 201,017 posts updated between 2015 and 2020 (2015-2020 dataset). (1) For model development, based on 10,000 randomly selected posts from 2009 to 2014 dataset, a human-based content analysis was performed to manually determine labels of each post (non-negative or negative attitudes). Then, a computer-based content analysis was conducted to automatically extract psycholinguistic features from each of the same 10,000 posts. Finally, a classification model for predicting negative attitudes was developed on selected features. (2) For model validation, on the population level, the developed model was implemented on remaining 727,849 posts from 2009 to 2014 dataset, and was externally validated by comparing proportions of negative attitudes between predicted and human-coded results. Besides, on the individual level, similar analyses were performed on 300 randomly selected posts from 2015 to 2020 dataset, and the developed model was externally validated by comparing labels of each post between predicted and actual results.

Results

For model development, the F1 and area under ROC curve (AUC) values reached 0.93 and 0.97. For model validation, on the population level, significant differences but very small effect sizes were observed for the whole sample (χ 2 1 = 32.35, p < 0.001; Cramer's V = 0.007, p < 0.001), men (χ 2 1 = 9.48, p = 0.002; Cramer's V = 0.005, p = 0.002), and women (χ 2 1 = 25.34, p < 0.001; Cramer's V = 0.009, p < 0.001). Besides, on the individual level, the F1 and AUC values reached 0.76 and 0.74.

Conclusion

This study demonstrates the efficiency and necessity of machine learning prediction of negative attitudes as a whole, and confirms that external validation is essential before implementing prediction models into practice.

Evidence Base Update of Psychosocial Treatments for Adolescents with Disruptive Behavior

OBJECTIVE

This article expands the review of psychosocial treatments for adolescents with disruptive behavior (DB), published previously by this journal. That earlier review focused on DB treatment studies published 1966-2014; the current paper updates the evidence base by incorporating DB treatment studies published 2014-2021.

METHOD

A literature search and screening process identified 63 new studies for inclusion in this updated review. The 63 new studies were combined with 86 studies from the prior review and evaluated using Journal of Clinical Child and Adolescent Psychology level of support criteria, which classify studies as well established, probably efficacious, possibly efficacious, experimental, or of questionable efficacy based on the evidence.

RESULTS

In total, 3 well-established, 7 probably efficacious, and 10 possibly efficacious treatments for adolescents with DB were identified. Further, 52 treatments were classified as experimental and 22 treatments were determined to have questionable efficacy.

CONCLUSIONS

There continues to be a large body of literature building the evidence base for treatments of adolescent DB. With a few exceptions, treatments falling into the top three evidence levels utilized more than one theoretical approach, enhancing each treatment's ability to target DB from multiple angles. Key advances include broad representation of various demographic groups, countries of origin, treatment settings, and provider types in this body of research. Despite these advances, more research is needed to address key gaps in the field, including the need for more studies on treatments tailored to adolescents with DB who are not yet involved with the juvenile justice system.