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This study aimed to assess the psychosocial determinants of psychological distress among people with disabilities in Ethiopia.
A cross-sectional study was conducted at an institution from 01 to 30 May 2021, using a census sampling approach.
A total of 269 individuals aged 18 and older with disabilities were present at the University of Gondar in Ethiopia.
The Kessler psychological distress scale (K10), the multidimensional scale of perceived social support, the actual help-seeking behaviour and the stigma scale for chronic illness-8 were used to assess the dependent and independent variables, respectively. Binary logistic regression analyses were performed; a p value less than 0.05 was considered statistically significant at a 95% CI.
In this study, the prevalence of psychological distress was 34.6% with a 95% CI (29.40 to 40.10). Factors, such as older age (adjusted ß=1.09; 95% CI 1.04 to 1.15), low perceived social support (adjusted OR (AOR)=1.83; 95% CI 1.16 to 2.89), experiencing stigma (AOR=2.50; 95% CI 1.12 to 5.61) and cognition problems (adjusted ß=0.73; 95% CI 0.62 to 0.85), were significantly associated with increased psychological distress. Of the participants with psychological distress, professional help-seeking behaviour was 7.5%.
Psychological distress was notably high among individuals with disabilities, while professional help-seeking remained very low. This underscores the urgent need for targeted mental health interventions to reduce stigma, strengthen social support and improve access to appropriate psychological care.
Artificial intelligence integration into healthcare has gained significant attention in recent years, with its use ranging from disease diagnosis to surgical assistance. While artificial intelligence's potential to improve patient outcomes and optimise patient care is undeniable, concerns regarding privacy, transparency, and the potential for medical errors persist. To take full advantage of artificial intelligence's transformative abilities, understanding patient perceptions and attitudes towards its integration into medicine is crucial for ethical considerations and health outcomes.
This study aimed to describe patients' perceptions of medical artificial intelligence and its integration into the healthcare system, drawing attention to a crucial yet understudied aspect of artificial intelligence adoption in Kazakhstan.
Descriptive qualitative design.
From February to March 2024, the researchers conducted semi-structured interviews amongst 13 patients. The interviews were audio-recorded, transcribed, translated, and then analysed using a thematic analysis approach. The study adhered to the COREQ guidelines.
Five themes emerged from 13 interviews: the benefits of artificial intelligence on patient care, the importance of human factors on patient care over artificial intelligence, revolutionising patient care delivery through artificial intelligence, patient education and artificial intelligence, and balancing technology and human interaction in artificial intelligence-driven intervention.
Patient perceptions of artificial intelligence integration into healthcare were primarily positive. Nevertheless, patients prefer artificial intelligence as a supplementary tool under human supervision due to risks such as possible medical errors and violations of patient privacy.
Patients provided the data for this study. The researchers interviewed them about their perceptions of medical artificial intelligence and its integration into the healthcare system. The patients or the public contributed nothing to the other aspects of the study.
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