Identification of factors associated with hospitalization in an outpatient population with mental health conditions: a case-control study.

Introduction: Addressing relevant determinants for preserved person-centered rehabilitation in mental health is still a major challenge. Little research focuses on factors associated with psychiatric hospitalization in exclusive outpatient settings. Some variables have been identified, but evidence across studies is inconsistent.

Differential associations between insight and quality-of-life dimensions among individuals with schizophrenia.

Purpose/objective: In schizophrenia, insight, the recognition that one has a medical illness that requires treatment, has long been related to deteriorated quality of life. Yet, insight and quality of life are broad constructs that encompass several dimensions. Here, we investigated differential associations between insight and quality-of-life dimensions using a psychological network approach.

Contribution of socio-demographic and clinical characteristics to predict initial referrals to psychosocial interventions in patients with serious mental illness.

Aims: Psychosocial rehabilitation (PSR) is at the core of psychiatric recovery. There is a paucity of evidence regarding how the needs and characteristics of patients guide clinical decisions to refer to PSR interventions. Here, we used explainable machine learning methods to determine how socio-demographic and clinical characteristics contribute to initial referrals to PSR interventions in patients with serious mental illness.

Functioning, clinical severity, education and sex moderate the inverse relationship between insight and quality of life in patients with schizophrenia.

Background: In schizophrenia, insight, the recognition that one has a medical illness that requires treatment, has long been related to deteriorated quality of life, a phenomenon that has been described as the "insight paradox". Here, we aimed to determine whether certain socio-demographic and clinical characteristics strengthen or weaken this negative relationship.

Methods: We used data from the French network of rehabilitation centers REHABase (N = 769).

Actionable Predictive Factors of Homelessness in a Psychiatric Population: Results from the REHABase Cohort Using a Machine Learning Approach.

Background: There is a lack of knowledge regarding the actionable key predictive factors of homelessness in psychiatric populations. Therefore, we used a machine learning model to explore the REHABase database (for rehabilitation database- = 3416), which is a cohort of users referred to French psychosocial rehabilitation centers in France.

Methods: First, we analyzed whether the different risk factors previously associated with homelessness in mental health were also significant risk factors in the REHABase.