Introduction: A significant gap between the number of individuals who need mental health care and the ones who actually have access to it has been consistently demonstrated in studies conducted in different countries. Recognizing the barriers to care and their contributions to delaying or preventing access to mental health services is a key step to improve the management of mental health care. The Barriers to Access to Care Evaluation (BACE) scale is a 30-item self-report instrument conceived to evaluate obstacles to proper mental health care. The main constraint in the investigation of these barriers in Brazil is the lack of a reliable instrument to be used in the Brazilian social and cultural context.

Objective: To describe the translation and adaptation process of the BACE scale to the Brazilian social and cultural context.

Method: The translation and adaptation process comprised the following steps: 1) translation from English to Brazilian Portuguese by two authors who are Brazilian Portuguese native speakers, one of whom is a psychiatrist; 2) evaluation, comparison and matching of the two preliminary versions by an expert committee; 3) back-translation to English by a sworn translator who is an English native speaker; 4) correction of the back-translated version by the authors of the original scale; 5) modifications and final adjustment of the Brazilian Portuguese version.

Results And Conclusion: The processes of translation and adaptation described in this study were performed by the authors and resulted in the Brazilian version of a scale to evaluate barriers to access to mental health care.

Download full-text PDF

Source
http://dx.doi.org/10.1590/2237-6089-2013-0022DOI Listing

Publication Analysis

Top Keywords

mental health
20
health care
16
barriers access
12
bace scale
12
brazilian social
12
social cultural
12
translation adaptation
12
brazilian portuguese
12
access care
8
care evaluation
8

Similar Publications

Rectangular Repetitive Transcranial Magnetic Monophasic vs Biphasic Stimulation for Major Depressive Disorder: A Randomized Controlled Pilot Trial.

Neuromodulation

January 2025

Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.

Objectives: Biphasic sinusoidal repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain stimulation treatment that has been approved by the US Food and Drug Administration for treatment-resistant depression (TRD). Recent advances suggest that standard rTMS may be improved by altering the pulse shape; however, there is a paucity of research investigating pulse shape, owing primarily to the technologic limitations of currently available devices. This pilot study examined the feasibility, tolerability, and preliminary efficacy of biphasic and monophasic rectangular rTMS for TRD.

View Article and Find Full Text PDF

The current study aims to determine how the interactions between practice (distributed/focused) and mental capacity (high/low) in the cloud-computing environment (CCE) affect the development of reproductive health skills and cognitive absorption. The study employed an experimental design, and it included a categorical variable for mental capacity (low/high) and an independent variable with two types of activities (distributed/focused). The research sample consisted of 240 students from the College of Science and College of Applied Medical Sciences at the University of Hail's.

View Article and Find Full Text PDF

The COVID-19 outbreak, caused by the SARS-CoV-2 virus, was linked to significant neurological and psychiatric manifestations. This review examines the physiopathological mechanisms underlying these neuropsychiatric outcomes and discusses current management strategies. Primarily a respiratory disease, COVID-19 frequently leads to neurological issues, including cephalalgia and migraines, loss of sensory perception, cerebrovascular accidents, and neurological impairment such as encephalopathy.

View Article and Find Full Text PDF

The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human-computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants.

View Article and Find Full Text PDF

Personalized Clustering for Emotion Recognition Improvement.

Sensors (Basel)

December 2024

Instituto de Estudios de Género, Universidad Carlos III de Madrid, Calle Madrid, 126, 28903 Getafe, Spain.

Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there are applications related to the safety and well-being of people (sexual assaults, gender-based violence, children and elderly abuse, mental health, etc.) that require even more improvements.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!