Background: Treatment of post- (physical restraint) patients with mental disorders has become a new problem in Indonesia in its effort to free the country from the physical restraint programme. Problems emerge when the patient returns to the family and society at large, and families that refuse to allow the patient to come back home risk the possibility that the patient may eventually become a psychotic vagrant.
Aims: To determine the appearance of families taking care of patients with mental disorders post-.
Method: This study was qualitative research using a case study approach. The number of participants from six families was selected by purposive sampling. Collecting data was done by in-depth interview, and analysed thematically using Colaizzi steps.
Results: The results showed that families coping when taking care of patients with mental disorders post- comprise seven themes. The seven themes are formed by four categories, 19 sub-themes and 32 sections.
Discussion: The appearance of coping was the overall description of coping in the form of strategic process stages, the support of coping and meaning for what the families feel when they are taking care of a patient with a mental disorder post-. The appearance of coping showed how the family chooses the mechanisms of coping to deal with stress and crisis.
Conclusion: The coping mechanisms that families use when taking care of a patient with a mental disorder post- were formed through stages of a strategic process. Families need coping strengthening interventions to provide optimal care for patients with mental disorders post-.
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http://dx.doi.org/10.1136/gpsych-2018-100035 | DOI Listing |
Glob Ment Health (Camb)
December 2024
The Warren Alpert Medical School of Brown University, Providence, RI, USA.
This study analyzes 2022 data from SAMHSA's Mental Health Client-Level Data (MH-CLD) to investigate ADHD prevalence and comorbidity. The findings reveal that 10.70% of the 5,899,698 patients were diagnosed with ADHD, indicating a high demand for targeted resources.
View Article and Find Full Text PDFGlob Ment Health (Camb)
December 2024
School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Psychosocial rehabilitation and psychosocial disability research have been a longstanding topic in healthcare, demanding continuous exploration and analysis to enhance patient and clinical outcomes. As the prevalence of psychosocial disability research continues to attract scholarly attention, many scientific articles are being published in the literature. These publications offer profound insights into diagnostics, preventative measures, treatment strategies, and epidemiological factors.
View Article and Find Full Text PDFGlob Ment Health (Camb)
November 2024
Department of Clinical Psychology, University of Dhaka, Dhaka, Bangladesh.
This study investigates the stigma against people with mental illness in Bangladesh through in-depth interviews with 14 patients and 9 healthcare professionals, and 33 focus group discussions with people without mental illness. The research has delved into the understanding of different types of stigma against mental illness in the context of Bangladesh. The findings revealed four types of stigma which were categorized into four themes namely self-stigma, public stigma, professional, and institutional stigma.
View Article and Find Full Text PDFFront Psychol
December 2024
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States.
Introduction: While the fact that visual stimuli synthesized by Artificial Neural Networks (ANN) may evoke emotional reactions is documented, the precise mechanisms that connect the strength and type of such reactions with the ways of how ANNs are used to synthesize visual stimuli are yet to be discovered. Understanding these mechanisms allows for designing methods that synthesize images attenuating or enhancing selected emotional states, which may provide unobtrusive and widely-applicable treatment of mental dysfunctions and disorders.
Methods: The Convolutional Neural Network (CNN), a type of ANN used in computer vision tasks which models the ways humans solve visual tasks, was applied to synthesize ("dream" or "hallucinate") images with no semantic content to maximize activations of neurons in precisely-selected layers in the CNN.
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