Ī plethora of factors can influence the level of care needed for an individual seeking psychological treatment. Therefore, the field has increasingly moved toward multivariate models that evaluate predictors in combination. Moreover, any predictor variable in isolation will have limited predictive utility given that individuals can score positively on some predictors and negatively on others. However, symptom severity alone, while useful, does not comprehensively capture all factors that predict treatment response or future need. The current STAND program utilizes a traditional method of assigning and adjusting level of care based primarily on symptom severity. STAND provides evidence-based, stratified stepped care for depression and anxiety, ranging from a self-guided online wellness program, to coach-guided online cognitive behavioral therapy (CBT), to clinician-delivered psychological and psychiatric care. The Screening and Treatment for Anxiety and Depression (STAND) program was developed as a part of the Depression Grand Challenge (DGC) at the University of California - Los Angeles (UCLA) to address the need for accessible and efficient evidence-based psychological treatments. Trial registrationĬ NCT05591937, submitted August 2022, published October 2022. Ultimately, findings will inform the practice of level of care triage and adaptation in psychological treatments, as well as the use of personalized mental health care broadly. Moreover, the developed multivariate decision-making algorithms may be used as a template in other community college settings. Results will provide a comparison on the traditional symptom severity decision-making and the novel multivariate decision-making with respect to treatment adherence, symptom improvement, and functional recovery. The multivariate decision-making algorithm will be updated annually to improve predictive outcomes. Participants will complete computerized assessments and self-report questionnaires at baseline and up to 40 weeks. After the initial triaging, level of care will be adapted throughout the duration of the treatment, utilizing either symptom severity or multivariate statistical approaches. For the symptom severity approach, initial triaging to level of care will be based on symptom severity, whereas for the multivariate approach, the triaging will be based on a comprehensive set of baseline measures. Participants will be recruited from a highly diverse sample of community college students. This trial will recruit a total of 1000 participants over the course of 5 years in the greater Los Angeles Metropolitan Area. The overarching goal is to evaluate whether the multivariate algorithm improves adherence to treatment, symptoms, and functioning above and beyond the symptom-based algorithm. The novel multivariate algorithm will be comprised of baseline (for triage and adaptation) and time-varying variables (for adaptation) in four areas: social determinants of mental health, early adversity and life stressors, predisposing, enabling, and need influences on health service use, and comprehensive mental health status. The stratified levels of care include a self-guided online wellness program, coach-guided online cognitive behavioral therapy, and clinician-delivered psychotherapy with or without pharmacotherapy. This randomized controlled trial will compare a traditional symptom severity decision-making algorithm to a novel multivariate decision-making algorithm for triage to and adaptation of mental health care. There is growing interest in using personalized mental health care to treat disorders like depression and anxiety to improve treatment engagement and efficacy.
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