Browsing by Author "Kessler, Ronald C."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Evidence for the Effectiveness of a National School-Based Mental Health Program in Chile(2015) Guzmán Piña, Javier; Kessler, Ronald C.; Squicciarini, Ana María; George, Myriam; Baer, Lee; Canenguez, Katia M.; Abel, Madelaine R.; McCarthy, Alyssa E.; Jellinek, Michael S.; Murphy, J. MichaelObjective Skills for Life (SFL) is the largest school-based mental health program in the world, screening and providing services to more than 1,000,000 students in Chile over the past decade. This is the first external evaluation of the program. Method Of the 8,372 primary schools in Chile in 2010 that received public funding, one-fifth (1,637) elected to participate in SFL. Each year, all first- and third-grade students in these schools are screened with validated teacher- and parent-completed measures of psychosocial functioning (the Teacher Observation of Classroom Adaptation–Re-Revised [TOCA-RR] and the Pediatric Symptom Checklist–Chile [PSC-CL]). Students identified as being at risk on the TOCA-RR in first grade are referred to a standardized 10-session preventive intervention in second grade. This article explores the relationships between workshop participation and changes in TOCA-RR and PSC-CL scores, attendance, and promotion from third to fourth grades. Results In all, 16.4% of students were identified as being at-risk on the TOCA-RR. Statistically significant relationships were found between the number of workshop sessions attended and improvements in behavioral and academic outcomes after controlling for nonrandom selection into exposure and loss to follow-up. Effect sizes for the difference between attending most (7–10) versus fewer (0–6) sessions ranged from 0.08 to 0.16 standard deviations. Conclusion This study provides empirical evidence that a large-scale mental health intervention early in schooling is significantly associated with improved behavioral and academic outcomes. Future research is needed to implement more rigorous experimental evaluation of the program, to examine longer-term effects, and to investigate possible predictors of heterogeneity of treatment responseItem Predicting posttraumatic stress disorder following a natural disaster(2018) Rosellini, Anthony J.; Dussaillant, Francisca; Zubizarreta, José R.; Kessler, Ronald C.; Rose, SherriEarthquakes are a common and deadly natural disaster, with roughly one-quarter of survivors subsequently developing posttraumatic stress disorder (PTSD). Despite progress identifying risk factors, limited research has examined how to combine variables into an optimized post-earthquake PTSD prediction tool that could be used to triages survivors to mental health services. The current study developed a post-earthquake PTSD risk score using machine learning methods designed to optimize prediction. The data were from a two-wave survey of Chileans exposed to the 8.8 magnitude earthquake that occurred in February 2010. Respondents (n = 23,907) were interviewed roughly three months prior to and again three months after the earthquake. Probable post-earthquake PTSD was assessed using the Davidson Trauma Scale. We applied super learning, an ensembling machine learning method, to develop the PTSD risk score from 67 risk factors that could be assessed within one week of earthquake occurrence. The super learner algorithm had better cross-validated performance than the 39 individual algorithms from which it was developed, including conventional logistic regression. The super learner also had a better area under the receiver operating characteristic curve (0.79) than existing post-disaster PTSD risk tools. Individuals in the top 5%, 10%, and 20% of the predicted risk distribution accounted for 17.5%, 32.2%, and 51.4% of all probable cases of PTSD, respectively. In addition to developing a risk score that could be implemented in the near future, these results more broadly support the utility of super learning to develop optimized prediction functions for mental health outcomes. (C) 2017 Elsevier Ltd. All rights reserved.