Literature Collection

BACKGROUND: Mental health legislation (MHL) is required to ensure a regulatory framework for mental health services and other providers of treatment and care, and to ensure that the public and people with a mental illness are afforded protection from the often-devastating consequences of mental illness. AIMS: To provide an overview of evidence on the significance of MHL for successful primary care for mental health and community mental health servicesMethod: A qualitative review of the literature on the significance of MHL for successful primary care for mental health and community mental health services was conducted. RESULTS: In many countries, especially in those who have no MHL, people do not have access to basic mental health care and treatment they require. One of the major aims of MHL is that all people with mental disorders should be provided with treatment based on the integration of mental health care services into the primary healthcare (PHC). In addition, MHL plays a crucial role in community integration of persons with mental disorders, the provision of care of high quality, the improvement of access to care at community level. Community-based mental health care further improves access to mental healthcare within the city, to have better health and mental health outcomes, and better quality of life, increase acceptability, reduce associated social stigma and human rights abuse, prevent chronicity and physical health comorbidity will likely to be detected early and managed. CONCLUSION: Mental health legislation plays a crucial role in community integration of persons with mental disorders, integration of mental health at primary health care, the provision of care of high quality and the improvement of access to care at community level. It is vital and essential to have MHL for every country.

PURPOSE: Timely identification of comorbidities is critical in sleep medicine, where large language models (LLMs) like ChatGPT are currently emerging as transformative tools. Here, we investigate whether the novel LLM ChatGPT o1 preview can identify individual health risks or potentially existing comorbidities from the medical data of fictitious sleep medicine patients. METHODS: We conducted a simulation-based study using 30 fictitious patients, designed to represent realistic variations in demographic and clinical parameters commonly seen in sleep medicine. Each profile included personal data (eg, body mass index, smoking status, drinking habits), blood pressure, and routine blood test results, along with a predefined sleep medicine diagnosis. Each patient profile was evaluated independently by the LLM and a sleep medicine specialist (SMS) for identification of potential comorbidities or individual health risks. Their recommendations were compared for concordance across lifestyle changes and further medical measures. RESULTS: The LLM achieved high concordance with the SMS for lifestyle modification recommendations, including 100% concordance on smoking cessation (κ = 1; p < 0.001), 97% on alcohol reduction (κ = 0.92; p < 0.001) and endocrinological examination (κ = 0.92; p < 0.001) or 93% on weight loss (κ = 0.86; p < 0.001). However, it exhibited a tendency to over-recommend further medical measures (particularly 57% concordance for cardiological examination (κ = 0.08; p = 0.28) and 33% for gastrointestinal examination (κ = 0.1; p = 0.22)) compared to the SMS. CONCLUSION: Despite the obvious limitation of using fictitious data, the findings suggest that LLMs like ChatGPT have the potential to complement clinical workflows in sleep medicine by identifying individual health risks and comorbidities. As LLMs continue to evolve, their integration into healthcare could redefine the approach to patient evaluation and risk stratification. Future research should contextualize the findings within broader clinical applications ideally testing locally run LLMs meeting data protection requirements.

OBJECTIVE: Sleep disturbance is an important feature of fetal alcohol spectrum disorder (FASD). We sought to describe sleep patterns in school-aged children with FASD, in comparison with a typically developing community group, and investigate the relationship between sleep and neurodevelopmental profiles. METHOD: The FASD cohort (N = 36) was recruited from a tertiary Australian FASD diagnostic center, and the typically developing group (N = 36) was previously recruited as a control cohort for a separate study. Sleep disturbance was assessed with the caregiver-completed Sleep Disturbance Scale for Children (SDSC) questionnaire. Neurodevelopmental assessment results for the 10 domains impaired in FASD were used for correlations with sleep disturbance. RESULTS: In the FASD group, 80% of children scored above the SDSC cutoff, compared with 22% of the control group ( p < 0.001). Statistically significant group differences were seen for all 6 subscales of the SDSC ( p < 0.05). The most frequently affected domains in the FASD group related to difficulties with initiating and maintaining sleep (58%), sleep-wake transition disorders (44%), and disorders of arousal (42%). A statistically significant relationship was not found between sleep and the severity of neurodevelopmental impairment or impairment of a particular domain, acknowledging the limitations of our small sample size. Half of the FASD sample (52%) were taking a pharmaceutical agent to support sleep, which was not associated with lower SDSC scores. CONCLUSION: In this small study, sleep disturbances were frequently reported by carers of children with FASD, independent of the severity of their neurodevelopmental impairments. Persistent sleep disturbance despite the use of sleep medications highlights the need for prospective studies exploring sleep interventions in this population. Integration of behavioral sleep medicine into management is recommended for all children with FASD.