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Background: Stigma resistance (SR) is defined as one's ability to deflect or challenge stigmatizing beliefs. SR is positively associated with patient's outcomes in serious mental illness (SMI). SR appears as a promising target for psychiatric rehabilitation as it might facilitate personal recovery. Objectives: The objectives of the present study are: (i) to assess the frequency of SR in a multicentric non-selected psychiatric rehabilitation SMI sample; (ii) to investigate the correlates of high SR Methods: A total of 693 outpatients with SMI were recruited from the French National Centers of Reference for Psychiatric Rehabilitation cohort (REHABase). Evaluation included standardized scales for clinical severity, quality of life, satisfaction with life, wellbeing, and personal recovery and a large cognitive battery. SR was measured using internalized stigma of mental illness – SR subscale. Results: Elevated SR was associated with a preserved executive functioning, a lower insight into illness and all recovery-related outcomes in the univariate analyses. In the multivariate analysis adjusted by age, gender and self-stigma, elevated SR was best predicted by the later stages of personal recovery [rebuilding; p = 0.004, OR = 2.89 (1.36–4.88); growth; p = 0.005, OR = 2.79 (1.30–4.43)). No moderating effects of age and education were found. Conclusion: The present study has indicated the importance of addressing SR in patients enrolled in psychiatric rehabilitation. Recovery-oriented psychoeducation, metacognitive therapies and family interventions might improve SR and protect against insight-related depression. The effectiveness of psychiatric rehabilitation on SR and the potential mediating effects of changes in SR on treatment outcomes should be further investigated in longitudinal studies. [ABSTRACT FROM AUTHOR]

Background. Self-stigma is a major issue in serious mental illness (SMI) and is negatively associated with patient outcomes. Most studies have been conducted in schizophrenia (SZ). Less is known about self-stigma in other SMI and autism spectrum disorder (ASD). The objectives of this study are: (i) to assess the frequency of self-stigma in a multicentric nonselected psychiatric rehabilitation SMI and ASD sample; and (ii) to investigate the correlates of elevated self-stigma in different SMI conditions and in ASD. Methods. A total of 738 SMI or ASD outpatients were recruited from the French National Centers of Reference for Psychiatric Rehabilitation cohort (REHABase). Evaluations included sociodemographic data, illness characteristics, and standardized scales for clinical severity, quality of life, satisfaction with life, wellbeing, personal recovery, a large cognitive battery, and daily functioning assessment. Results. 31.2%of the total sample had elevated self-stigma. The highest prevalence (43.8%) was found in borderline personality disorder and the lowest (22.2%) in ASD. In the multivariate analysis, elevated self-stigma was best predicted by early stages of personal recovery (moratorium, p = 0.001, OR = 4.0 [1.78-8.98]; awareness, p = 0.011, OR = 2.87 [1.28-6.44]), history of suicide attempt (p = 0.001, OR = 2.27 [1.37-3.76]), insight (p = 0.002, OR = 1.22 [1.08-1.38]), wellbeing (p = 0.037, OR = 0.77 [0.60-0.98]), and satisfaction with interpersonal relationships (p < 0.001, OR = 0.85 [0.78-0.93]). Conclusions. The present study has confirmed the importance of addressing self-stigma in SMI and ASD patients enrolled in psychiatric rehabilitation. The effectiveness of psychiatric rehabilitation on self-stigma and the potential mediating effects of changes in self-stigma on treatment outcomes should be further investigated. [ABSTRACT FROM AUTHOR]

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., Research Method/design: We extracted data from the French network of rehabilitation centers REHABase (January 2016 to December 2022, N = 1,056). Our psychological network analysis modeled insight and quality of life as a network of interacting dimensions: three insight dimensions (awareness of illness, reattribution of symptoms to the disease, and recognition of treatment need) and eight quality-of-life dimensions (autonomy, physical and psychosocial well-being, relationships with family, friends and romantic partners, resilience, and self-esteem)., Results: Insight was negatively associated with quality of life. Our psychological network analysis revealed a strong negative association between awareness of disease and self-esteem. Both dimensions were the strongest nodes in the overall network. Our network analysis also revealed a significant but positive connection between recognition of treatment needs and resilience., Conclusion/implications: While insight and quality of life are overall negatively associated, we found both negative and positive connections between insight and quality-of-life dimensions. The negative relationship between insight and quality of life may reflect the deleterious effects of diagnostic labeling on a patient's self-esteem. Yet, acknowledgment of treatment needs may have positive effects on quality of life and may promote recovery, perhaps because it emphasizes the need for support rather than labels and abnormalities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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). We explored mean differences in quality-of-life scores between patients with good insight vs. poor insight. We also explored modifying effects of socio-demographic and clinical characteristics (sex, education, age, functioning, clinical severity, duration of illness)., Results: Patients with good insight had a decreased quality-of-life total score. Similar effects were found for the following sub-dimensions of quality of life: autonomy, physical and psychological well-being, and self-esteem. The negative effect of insight on quality of life was attenuated for people who had >12 years of education and for people with a higher level of functioning. By contrast, the negative effect of insight on quality of life was accentuated in people with greater clinical severity. Functioning and clinical severity showed similar modifying effects for other quality-of-life dimensions: autonomy, physical and psychological well-being, and self-esteem. Finally, males demonstrated an increased negative association between insight and self-esteem., Conclusions: The relationship between insight and quality of life is moderated by socio-demographic and clinical circumstances. Future inquiries may utilize our findings by integrating socio-demographic and clinical factors in treatment programs designs to conjointly improve insight and quality of life., Competing Interests: Declaration of competing interest None., (Copyright © 2023. Published by Elsevier B.V.)

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., Methods: Data were extracted from the French network of rehabilitation centres, REHABase, collected between years 2016 and 2022 and analysed between February and September 2022. Participants presented with serious mental illnesses, including schizophrenia spectrum disorders, bipolar disorders, autism spectrum disorders, depressive disorders, anxiety disorders and personality disorders. Information from 37 socio-demographic and clinical variables was extracted at baseline and used as potential predictors. Several machine learning models were tested to predict initial referrals to four PSR interventions: cognitive behavioural therapy (CBT), cognitive remediation (CR), psychoeducation (PE) and vocational training (VT). Explanatory power of predictors was determined using the artificial intelligence-based SHAP (SHapley Additive exPlanations) method from the best performing algorithm., Results: Data from a total of 1146 patients were included (mean age, 33.2 years [range, 16-72 years]; 366 [39.2%] women). A random forest algorithm demonstrated the best predictive performance, with a moderate or average predictive accuracy [micro-averaged area under the receiver operating curve from 'external' cross-validation: 0.672]. SHAP dependence plots demonstrated insightful associations between socio-demographic and clinical predictors and referrals to PSR programmes. For instance, patients with psychotic disorders were more likely to be referred to PE and CR, while those with non-psychotic disorders were more likely to be referred to CBT and VT. Likewise, patients with social dysfunctions and lack of educational attainment were more likely to be referred to CR and VT, while those with better functioning and education were more likely to be referred to CBT and PE., Conclusions: A combination of socio-demographic and clinical features was not sufficient to accurately predict initial referrals to four PSR programmes among a French network of rehabilitation centres. Referrals to PSR interventions may also involve service- and clinician-level factors. Considering socio-demographic and clinical predictors revealed disparities in referrals with respect to diagnoses, current clinical and psychological issues, functioning and education.

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- n = 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. In the second step, we used unbiased classification and regression trees to determine the key predictors of homelessness. Post hoc analyses were performed to examine the importance of the predictors and to explore the impact of cognitive factors among the participants., Results: &nbsp;First, risk factors that were previously found to be associated with homelessness were also significant risk factors in the REHABase. Among all the variables studied with a machine learning approach, the most robust variable in terms of predictive value was the nature of the psychotropic medication (sex/sex relative mean predictor importance: 22.8, σ = 3.4). Post hoc analyses revealed that first-generation antipsychotics (15.61%; p < 0.05 FDR corrected), loxapine (16.57%; p < 0.05 FWER corrected) and hypnotics (17.56%; p < 0.05 FWER corrected) were significantly associated with homelessness. Antidepressant medication was associated with a protective effect against housing deprivation (9.21%; p < 0.05 FWER corrected)., Conclusions: Psychotropic medication was found to be an important predictor of homelessness in our REHABase cohort, particularly loxapine and hypnotics. On the other hand, the putative protective effect of antidepressants confirms the need for systematic screening of depression and anxiety in the homeless population.

Background: Female sex/gender has been associated with better longitudinal outcomes in schizophrenia spectrum disorders (SSDs). Few studies have investigated the relationships between female gender and recovery-related outcomes. Women's specific psychiatric rehabilitation needs remain largely unknown., Objective: The objectives of the present study are to investigate sex differences in (1) objective and subjective aspects of recovery and (2) psychiatric rehabilitation needs in a multicenter non-selected psychiatric rehabilitation SSD sample., Methods: 1,055 outpatients with SSD (DSM-5) were recruited from the French National Centers of Reference for Psychiatric Rehabilitation (REHABase) cohort between January 2016 and November 2019. Evaluation included standardized scales for quality of life, satisfaction with life, and well-being and a broad cognitive battery. Socially valued roles at enrollment were recorded. Functional recovery was measured using the Global Assessment of Functioning scale (GAF) and personal recovery with the Stages of Recovery Instrument (STORI)., Results: Female sex was the best predictor of having more than 2 socially valued roles in the multivariate analysis (P < .001; OR [95% CI] = 5.42 [2.34-13.06]). No sex differences were found for functional recovery or personal recovery. Female gender was positively associated with self-stigma (P = .036) and suicidal history (P < .001) and negatively correlated with quality of life (P = .004) and satisfaction with interpersonal relationships (P = .029), an area in which women reported more unmet needs (P = .004)., Conclusions: The present study found that women had poorer subjective recovery-related outcomes and more unmet needs than men. It would therefore be beneficial to develop recovery-oriented interventions addressing women's specific needs and implement these in psychiatric rehabilitation services., (© Copyright 2021 Physicians Postgraduate Press, Inc.)

Objective: The REHABase project is a French observational, prospective, and multicenter cohort study of serious mental illness and autism spectrum disorder (ASD), launched in 2016 for a planned minimum duration of 15 years. The aim is to characterize the care and quality-of-life needs of participants. This article presents initial results from data collection., Methods: Psychosocial, cognitive, and functional data were collected at baseline, annually, and after rehabilitation care. Data from the baseline evaluation on diagnoses, medications, well-being, insight, life satisfaction, and care needs are presented. The clinical profiles of REHABase participants with serious mental illness or ASD were assessed in relation to their level of satisfaction with life and well-being in nine life dimensions and their needs, according to their stage of recovery in a five-stage model., Results: Baseline data were collected for 1,397 participants between January 2016 and August 2018. Main diagnoses were schizophrenia spectrum disorder (49%); ASD (13%); and personality (12%), bipolar (9%), and major depressive (6%) disorders. More than 50% of participants reported needs for care or interventions in four of nine dimensions: employment, cognitive functioning, symptom management, and interpersonal relationships. Nearly half of participants were not in the active stages of recovery (stages 4 and 5), and even those considered to have reached the final stage continued to require help in several areas., Conclusions: Most participants had already received psychiatric care for several years, and most remained dissatisfied with their social and emotional life and their psychological well-being.