Evaluating transdiagnostic, evidence-based mental health care in a safety-net setting serving homeless individuals.
Shannon Sauer-ZavalaAmantia A AmetajJulianne G WilnerKate H BentleySantiago MarquezKaylie A PatrickBillie StarksDerri ShtaselLuana MarquesPublished in: Psychotherapy (Chicago, Ill.) (2018)
Homeless individuals experience higher rates of mental illness than the general population, though this group is less likely to receive evidence-based psychological treatment for these difficulties. One explanation for this science-to-service gap may be that most empirically supported interventions are designed to address a single disorder, which may not map on to the substantial comorbidity present in safety-net samples, and create a high training burden for often underresourced clinicians who must learn multiple protocols to address the needs of their patients. One solution may be to prioritize the dissemination of transdiagnostic interventions that can reduce therapist burden and simultaneously address comorbid conditions. The purpose of the present article is to describe the process of conducting a pilot study administering the Unified Protocol (UP), a transdiagnostic treatment for the range of emotional disorders, at a community-based organization that provides health care and other services to homeless individuals and families in Boston, Massachusetts. Therapists on a specialized behavioral health unit received didactic training in the intervention, followed by weekly consultation while they provided the UP to patients on their caseload. Qualitative and quantitative data were collected from both patients and therapists. Barriers to use of the UP by therapists, as well as to conducting research in this setting, will be discussed, along with the solutions that were used. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Keyphrases
- mental illness
- healthcare
- end stage renal disease
- mental health
- newly diagnosed
- chronic kidney disease
- ejection fraction
- public health
- prognostic factors
- primary care
- systematic review
- emergency department
- high resolution
- risk factors
- patient reported outcomes
- machine learning
- mass spectrometry
- big data
- artificial intelligence
- smoking cessation
- health information
- drug induced
- patient reported
- human health
- sleep quality
- health promotion