Exploring Therapeutic Outcomes Through Dyadic Interactions: The Role of Patient-Avatar Dynamics in Avatar Therapy.

Objective: Despite the efficacy of current therapies, a significant proportion of patients with schizophrenia, a complex mental disorder marked by both positive (present) and negative (absent) symptoms, are considered to have treatment-resistant schizophrenia. Avatar therapy (AT) allows patients to interact with a three-dimensional representation of their most distressing voices in a virtual reality setting. The therapy shows promise in reducing impairments and improving quality of life through the establishment of a therapeutic alliance and the exploration of dyadic interactions (verbal exchanges) between patients and their avatar.

[The impact of cannabis on psychiatric symptoms: A cross-sectional study on people with severe mental disorder].

Objective Cannabis is the most commonly used drug in the general population, but its prevalence of use remains higher among people suffering from severe mental disorders. Nevertheless, current cannabis research showed it to be deleterious on psychiatric symptoms, especially among patients with severe mental disorders. This present cross-sectional study aims to evaluate the impact of cannabis consumption on the psychiatric symptomatology of people with a serious mental disorder by controlling for the confounding variables of age, sex and concomitant alcohol or stimulant consumption.

Avatar Intervention in Virtual Reality for Cannabis Use Disorder in Individuals With Severe Mental Disorders: Results From a 1-Year, Single-Arm Clinical Trial.

Background: The dual diagnosis of cannabis use disorder (CUD) and severe mental disorder (SMD) results in clinically complex individuals. Cannabis use is known to have negative consequences on psychiatric symptoms, medication compliance, and disease prognosis. Moreover, the effectiveness of currently available psychotherapeutic treatments is limited in this population.

Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review.

Background: An increasing body of literature highlights the integration of machine learning with genomic data in psychiatry, particularly for complex mental health disorders such as schizophrenia. These advanced techniques offer promising potential for uncovering various facets of these disorders. A comprehensive review of the current applications of machine learning in conjunction with genomic data within this context can significantly enhance our understanding of the current state of research and its future directions.

Seclusion and mechanical restraint in the wake of the COVID-19 pandemic: an increased use in mental health settings.

Purpose: COVID-19 pandemic-related restrictions have significantly changed the environment and the delivery of direct care in all health services, including psychiatric hospitals. The aim of the study is two-fold: 1) to retrospectively assess the incidence of seclusion and mechanical restraint in a Quebec mental health hospital over 4 years; and 2) to assess the impact of the COVID-19 pandemic on their incidence.

Methods: We conducted a retrospective study based on medical records from a Quebec mental health hospital collected (a) from April 2016 to March 2019), (b) from April 2019 to March 2020 (pre-COVID onset), and (c) from April 2020 to March 2021 (post-COVID onset).