Literature DB >> 35245744

A Brazilian bottom-up strategy to address mental health in a diverse population over a large territorial area - an inspiration for the use of digital mental health.

Natália Bezerra Mota1, Juliana Pimenta2, Maria Tavares2, Leonardo Palmeira2, Alexandre Andrade Loch3, Cecília Hedin-Pereira4, Elisa C Dias5.   

Abstract

Brazil is a continental country with a history of massive immigration waves from around the world. Consequently, the Brazilian population is rich in ethnic, cultural, and religious diversity, but suffers from tremendous socioeconomic inequality. Brazil has a documented history of categorizing individuals with culturally specific behaviors as mentally ill, which has led to psychiatric institutionalization for reasons that were more social than clinical. To address this, a "network for psychosocial care" was created in Brazil, that included mental health clinics and community services distributed throughout the country. This generates local support for mental health rehabilitation, integrating psychiatric care, family support and education/work opportunities. These clinics and community services are tailored to provide care for each specific area, and are more attuned to regional culture, values and neighborhood infrastructure. Here we review existing reports about the Brazilian experience, including advances in public policy on mental health, and challenges posed by the large diversity to the psychosocial rehabilitation.  In addition, we show how new digital technologies in general, and computational speech analysis in particular, can contribute to unbiased assessments, resulting in decreased stigma and more effective diagnosis of the mental diseases, with methods that are free of gender, ethnic, or socioeconomic biases.
Copyright © 2022. Published by Elsevier B.V.

Entities:  

Keywords:  Computational psychiatry; Diversity; Psychiatric rehabilitation; Public policy; Social stigma

Mesh:

Year:  2022        PMID: 35245744     DOI: 10.1016/j.psychres.2022.114477

Source DB:  PubMed          Journal:  Psychiatry Res        ISSN: 0165-1781            Impact factor:   3.222


  3 in total

1.  Translating Natural Language Processing into Mainstream Schizophrenia Assessment.

Authors:  Brita Elvevåg; Alex S Cohen
Journal:  Schizophr Bull       Date:  2022-09-01       Impact factor: 7.348

2.  The Mining Method of Ideological and Political Elements in University Public Mental Health Courses Based on Artificial Intelligence Technology.

Authors:  Fangfang Li; Le Gu; Hongchao Xu
Journal:  J Environ Public Health       Date:  2022-08-31

3.  The English Teaching Methods in the Field of Public Health in Colleges and Universities Based on Artificial Intelligence Technology.

Authors:  Shan Li
Journal:  J Environ Public Health       Date:  2022-09-16
  3 in total

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