Psychiatric inpatient care is a high-resource component of mental health systems. Understanding the diagnoses driving inpatient care is essential for evidence-based service planning. This study describes psychiatric inpatient bed use in Ireland from 2013 to 2023 by diagnosis, sex, and age group.

National descriptive analyses using aggregated data from the National Psychiatric Inpatient Reporting System, covering acute inpatient admissions in Ireland. The outcome was total number of inpatient bed days, categorised by ICD-10 diagnostic group. Primary analyses focused on public services, with private services reported separately. We additionally report on child and adolescent mental health services (CAMHS), adult services, and sex-stratified results.

Between 2013 and 2023, there were k=3,993,628 public and k=1,879,877 private inpatient bed days. In public services, schizophrenia spectrum disorders accounted for 41.9% of bed days (k=1,673,529), more than twice as many as the next most prevalent diagnosis. Men accounted for 51.2% of acute public bed days. In private adult services, depressive disorders accounted for the largest proportion of bed days (28.5%, k=513,166). In public and private CAMHS, eating disorders accounted for the largest proportion of bed days (25.6%, k=46,850 and 24.4%, k=18,775), respectively.

This national analysis demonstrates that schizophrenia spectrum disorders represent, by far, the largest diagnostic group, accounting for 42% of public acute psychiatric inpatient beds days, highlighting the sustained, intensive service demands associated with these disorders. These data provide a clear empirical basis for future service planning, highlighting where targeted enhancements to community-based care could most effectively reduce the need for hospitalisation.

Randomised controlled trials have demonstrated that treatment with glucagon-like peptide-1 receptor agonists (GLP-1RAs) reduces body weight, waist circumference and haemoglobin A1c (HbA1c) levels in patients with schizophrenia. However, because of participant selection in clinical trials, it remains unclear whether the beneficial metabolic effects of GLP-1RAs for patients with schizophrenia extend to real-world clinical settings.

We investigated whether GLP-1RA treatment initiation was associated with reductions in HbA1c, low-density lipoprotein (LDL) and triglyceride levels (the latter two as proxies for weight loss) in individuals with schizophrenia.

We used the Danish nationwide registers to identify individuals with schizophrenia between 1996 and 2023. Among those, we identified individuals initiating GLP-1RA treatment. The main outcomes were changes in HbA1c, LDL and triglyceride levels following initiation of GLP-1RA treatment. Changes in HbA1c, LDL and triglyceride levels were visualised graphically and quantified with interrupted time-series analyses.

We identified 3042 individuals with schizophrenia who initiated a GLP-1RA. Initiation of GLP-1RA treatment for diabetes was followed by reductions in HbA1c, LDL and triglyceride levels of 11.4, 6.2 and 14.8%, respectively. Initiation of GLP-1RA treatment for overweight was followed by reductions in HbA1c, LDL and triglyceride levels of 3.4, 4.9 and 12.7%, respectively. All level changes were statistically significant (p < 0.001).

Initiation of GLP-1RA treatment was followed by clinically meaningful reductions in HbA1c, LDL and triglyceride levels among individuals with schizophrenia. These findings suggest that the metabolic benefits of GLP-1RA treatment found in randomised clinical trials in schizophrenia extend to real-world clinical settings.

International guidance recommends that long-acting injectable (LAI) antipsychotics be considered for routine management of first-episode schizophrenia. Reliable national estimates of utilisation are lacking for most countries, including Ireland.

To determine the prevalence of LAI antipsychotic prescribing in Ireland between 2014 and 2024 and explore the effects of age, sex and socioeconomic status on prescribing trends.

The annual prescribing prevalence was calculated per 1000 individuals with schizophrenia aged 15–69 using the national pharmacy claims database. The effects of year, age, sex, socioeconomic class and antipsychotic medication on LAI antipsychotic prescribing trends were examined using log-linear regression.

The prevalence of LAI antipsychotic prescribing decreased from 327/1000 in 2014 to 281/1000 in 2024, with an average annual change in prescribing rate of −1.49% [95% CI −1.98, −0.79]. In 2024, paliperidone LAIs were most commonly prescribed (40.2%). Prescribing prevalence increased with age, with the lowest prescribing prevalence occurring among those aged 15–24 years (79/1000) and the highest among those aged 65–69 years (434/1000). The prescribing prevalence increased significantly among those aged 25–34 years only (+2.7% [95% CI 1.7, 4.3]). Prescribing rates decreased across all ages between 45 and 69 years; the largest decrease occurred among those aged 65–69 years (−4.3% [95% CI −5.7, −2.9]). Higher rates of prescribing were seen in male compared with female patients across all age bands, with the highest male-to-female ratio (2.94) occurring among those aged 25–34 years.

Approximately 28% of Irish individuals with schizophrenia are prescribed an LAI antipsychotic. Prescribing rates have decreased by 14% since 2014. Priorities for future research include implementation science studies to identify strategies that can effectively increase LAI antipsychotic prescribing.

The application of artificial intelligence (AI) to resting-state electroencephalography (EEG) data represents a burgeoning frontier in schizophrenia diagnostics. However, the inferential validity of findings is frequently compromised by methodological heterogeneity. This systematic review rigorously evaluates the diagnostic efficacy and methodological robustness of AI-generated EEG biomarkers by implementing the Prediction model Risk Of Bias Assessment Tool for Artificial Intelligence (PROBAST+AI).

Following PRISMA guidelines, a systematic search of PubMed, Scopus, and Web of Science identified 43 primary studies encompassing 3,420 patients with schizophrenia or first-episode psychosis and 3,164 healthy controls (total N = 6,584). Methodological quality and validation strategies were assessed using the 34 signaling questions of the PROBAST+AI framework.

The aggregate mean diagnostic accuracy across the literature was 93.38%, with deep learning architectures demonstrating superior metrics relative to classical machine learning (96.83% vs. 87.68%). Critically, external validation was restricted to 4.7% of the included studies. A pronounced performance discrepancy was observed between subject-wise (88.22%) and segment-wise (98.14%) validation protocols, indicating pervasive data leakage. Overall, 76.7% of the analyzed studies were characterized by a high risk of bias, predominantly stemming from suboptimal data partitioning and inadequate independent verification.

Contemporary diagnostic accuracies appear to be artifactually elevated due to structural methodological deficiencies and data leakage. Consequently, current evidence does not support the immediate clinical implementation of AI-EEG biomarkers in schizophrenia. To facilitate translational progress, we propose a standardized methodological framework optimizing validation strategies.

Major psychiatric disorders (MPDs) and premenstrual disorders (PMDs) are often comorbid, with indications of shared genetic architecture. However, the specific genetic variants underlying this comorbidity remain elusive. The aim of this study was to identify genomic loci shared between MPDs and PMDs, and to assess the functional characteristics of genes mapped to these loci.

We used genome-wide association data for symptoms of PMDs (S-PMD, n = 67 068) from the Norwegian Mother, Father and Child Cohort Study (MoBa), and for MPDs (schizophrenia, n = 130 644; bipolar disorder, n = 827 335; major depression, n = 1 918 432) from the Psychiatric Genomic Consortium. Shared genetic loci were identified with the conjunctional false discovery rate (conjFDR). Genes mapped to the identified loci were included in functional and pharmacological gene-set enrichment analyses (GSEAs).

We identified 20 loci shared between S-PMD and the MPDs, including 19 novel loci for S-PMD (conjFDR<0.05). GSEA revealed genes involved in calcium signaling and neuronal projection pathways. The drug enrichment analysis identified 36 drugs, including riluzole and mianserin, significantly associated with the identified molecular pathways.

We identified 20 genetic loci shared between the S-PMD and MPDs, implicating calcium transmission in the pathogenesis of PMDs. Together with the drug enrichment results, this provides a biological basis for repurposing existing drugs in the treatment of PMD.

The psychosis continuum conceptualises psychotic phenomena as existing on a spectrum from subclinical experiences to psychotic disorders. Despite epidemiological and clinical evidence in support of this model, few studies have examined polygenic liability across the severity continuum: from subclinical experiences to first-episode psychosis (FEP).

To map polygenic liability for schizophrenia (SCZ), bipolar disorder and major depressive disorder (MDD) across the psychosis continuum. We hypothesised that schizophrenia polygenic scores (SCZ-PGS) would track phenotypic severity most closely across this continuum.

We harmonised data from 5 south-east London cohorts (N = 1315; 860 European, 455 African ancestries) across 4 ordered groups indicative of different levels of phenotypic severity: controls (n = 647), individuals with self-reported psychotic experiences (n = 89), individuals at clinical high risk (CHR) that did not convert to psychosis (n = 124) and individuals with FEP (n = 455). We computed ancestry-specific PGS for SCZ, bipolar disorder and MDD and compared these across groups using ordinal logistic regression.

In European ancestry participants, elevated SCZ-PGS (odds ratio 1.72, p = 4.57 × 10−15), bipolar disorder-PGS (odds ratio 1.56, p = 1.03 × 10−10) and MDD-PGS (odds ratio 1.44, p = 4.86 × 10−8) were individually associated with increasing phenotypic severity. SCZ-PGS explained the largest variance (R2 = 0.084) across the psychosis continuum and showed pairwise differences between CHR and controls (β = 0.32, p = 0.027), and between FEP and CHR (β = 0.38, p = 0.017). Multivariable models, combining three PGS, outperformed univariable models (R2 = 0.105). Similar trends in SCZ-PGS across the psychosis continuum phenotype were seen in African ancestry participants, but only controls and FEP groups differed significantly (β = 0.27, p = 0.037).

Our findings provide biological evidence supporting the psychosis continuum framework. In European ancestry, SCZ-PGS differences suggest that CHR who do not transition to psychosis have intermediate genetic vulnerability. Multivariable models, utilising all three PGS, explained additional variance compared with univariable ones, supporting transdiagnostic genetic liability. Improving PGS predictive power in African ancestry remains a priority.

Schizophrenia (SCZ) is a heterogeneous disorder characterized by widespread brain dysconnectivity, yet it remains unclear whether variable structural dysconnectivity patterns reflect biologically and clinically meaningful subtypes.

We recruited 105 SCZ patients and 97 healthy controls, collecting magnetic resonance imaging, clinical assessment, and laboratory test data. Double generalized linear models were first applied to estimate group differences in the mean and inter-individual variability of structural connectivity between SCZ and controls. Subsequently, nonnegative matrix factorization was used to identify SCZ subtypes according to dysconnectivity profiles.

We observed a significantly altered subnetwork that exhibited increased inter-individual variability in SCZ, with its connections and regions predominantly enriched in the ventral-attention, frontal–parietal, and default-mode networks that support higher-order cognition. Connectivity-derived subtype analysis grouped patients into two subtypes, with subtype-II tending to show more severe positive symptoms (t = 2.272, nominal P = 0.025), a lower neutrophil/lymphocyte ratio (t = −2.383, nominal P = 0.025), a better treatment response (t = 2.366, nominal P = 0.021), and greater short-term symptom improvement (t = 3.509, nominal P = 0.006) within the olanzapine monotherapy subgroup. This subtype also demonstrated distinct neuroanatomical features, including a subtype-specific cortical thickness change pattern unrelated to SCZ or bipolar disorder, as well as subtype-specific brain epicenters, such as left posterior cingulate and rostral middle frontal regions.

Our study demonstrates increased inter-individual variability in the structural connectome of SCZ and highlights the potential of connectome-based subtyping approaches for characterizing disease heterogeneity related to pathophysiological processes.

Electroconvulsive therapy (ECT) is an established treatment for schizophrenia. Oxidative stress has been implicated in its pathophysiology, and Thiobarbituric acid reactive substances (TBARS) are widely used markers of lipid peroxidation. However, the effect of ECT on TBARS levels remains unclear.

To compare changes in TBARS levels in schizophrenia inpatients receiving ECT plus pharmacotherapy versus pharmacotherapy alone and to examine associations between TBARS levels and clinical variables.

Thirty-eight inpatients with schizophrenia were included (ECT + pharmacotherapy: n = 18; pharmacotherapy only: n = 20). Serum TBARS levels were measured at admission and discharge. Clinical severity was assessed using the Clinical Global Impression and Positive and Negative Syndrome Scale. Non-parametric analyses and multiple linear regression were conducted.

TBARS levels did not significantly change from admission to discharge in either the ECT group (median 756.5 vs. 768.5 nmol/L) or the pharmacotherapy group (median 644.0 vs. 693.5 nmol/L). Percentage changes were comparable between groups (9.64% vs. 15.34%). TBARS levels were negatively correlated with age (ρ = −0.441) and illness duration (ρ = −0.333). Regression analysis showed that suicide history was the only significant predictor of TBARS levels (β = −0.402), while treatment modality was not associated with TBARS change.

ECT was not associated with statistically significant changes in TBARS levels; however, these findings should be interpreted cautiously given the exploratory nature of the study, the small sample size, and methodological limitations. The observed associations with clinical variables suggest that oxidative stress markers may have relevance in specific patient subgroups, warranting further investigation.

Schizophrenia is associated with widespread abnormalities in large-scale brain organization; however, the mechanisms linking altered cortical hierarchy to disrupted neural communication and clinical symptoms remain unclear. By integrating functional connectivity gradient analysis and stepwise functional connectivity (SFC), we aimed to clarify how alterations in cortical hierarchy translate into disrupted connectome-based propagation in schizophrenia.

Resting-state functional MRI data from 820 participants (310 healthy controls and 510 patients with schizophrenia) were analyzed across multiple cohorts. Cortical hierarchy was characterized using functional connectivity gradients, focusing on the principal unimodal–transmodal gradient (G1). Connectome-based propagation was examined using SFC, with step counts indexing propagation efficiency across predefined hierarchical levels. Associations with clinical symptoms were assessed, including mediation analyses.

Global cortical hierarchy was preserved in schizophrenia; however, selective alterations were evident. Patients with schizophrenia showed significantly reduced G1 values in the dorsal attention network (DAN), with regional alterations primarily localized to dorsal attention and somatomotor regions. SFC analysis revealed delayed propagation toward higher-order association networks in schizophrenia, reflected by increased step counts to intermediate and transmodal hierarchical levels. Among patients with illness duration <5 years, delayed propagation to higher hierarchical levels was associated with greater positive and negative symptom severity. Mediation analyses demonstrated that delayed propagation significantly mediated the relationship between DAN hierarchical positioning and clinical symptoms.

These findings suggest that schizophrenia is characterized by selective disruption of cortical hierarchy within attentional control systems, leading to impaired large-scale connectome-based propagation that links cortical organization to clinical symptoms.

Antipsychotics are used to manage hostility and aggressive behaviour in schizophrenia, but their comparative efficacy remains unclear.

To compare the efficacy of antipsychotics for reducing hostility and aggressive behaviour in schizophrenia.

We searched PubMed, PsycINFO, ClinicalTrials.gov, and the Cochrane Schizophrenia Group’s register to 25 April 2025. Two reviewers screened studies and extracted data. We included randomised controlled trials in adults with schizophrenia or related psychotic disorders assessing hostility or aggressive behaviour with ≥48 h follow-up. Standardised mean differences (SMDs) were synthesised in a frequentist random-effects network meta-analysis. Confidence was assessed using Confidence In Network Meta-Analysis. The protocol was registered in PROSPERO (CRD42024572272).

Forty-one studies involving 21 025 participants and 18 antipsychotics were included. Most studies were based on double-blind, parallel-group trials (37/41, 90%), with a median follow-up of 8 weeks (interquartile range = 6). The most common outcome measures were hostility items or related subscales from symptom rating scales (36/41, 88%). Compared with placebo, several antipsychotics reduced hostility and aggressive behaviour. Clozapine (SMD –0.75, 95% CI –1.13 to –0.38), paliperidone (–0.62, –0.88 to –0.36), and olanzapine (–0.60, –0.86 to –0.35) showed the largest effects. Smaller but significant effects were observed for asenapine, risperidone, aripiprazole and haloperidol (SMD range –0.52 to –0.30). Confidence in the evidence was low or very low, mainly due to within-study bias and incoherence.

Antipsychotics reduced hostility and aggressive behaviour in predominantly short- to medium-term studies. Clozapine, paliperidone and olanzapine appeared most effective, although low-certainty evidence limits conclusions about differences between antipsychotics. Further trials with aggressive behaviour as the primary outcome and longer follow-up are needed.

Individuals with schizophrenia spectrum disorders (SSDs) frequently exhibit low levels of physical activity (PA) and mood disturbances, both of which contribute to functional impairment and poorer long-term outcomes. Despite growing evidence linking PA and affective regulation, little is known about the real-time, bidirectional relationship between these domains in SSD.

In this multicenter observational study (DiAPAson project), 120 patients with SSD and 113 age- and sex-matched healthy controls (HC) underwent a 7-day ecological assessment combining smartphone-based Ecological Momentary Assessment (EMA; 8 prompts/day) and continuous wrist-worn actigraphy. We analyzed the dynamic associations between PA and mood using generalized linear mixed models (GLMMs) and Bayesian models with lagged temporal structures.

GLMMs revealed significantly lower daily mood in SSD compared to HC (estimate = −0.33, 95% CI: −0.53 to −0.12; p = .002), but no significant day-level association between PA and mood. In contrast, Bayesian models uncovered robust within-day, bidirectional associations in HC such that higher PA levels were followed by higher subsequent mood and, conversely, better mood predicted higher subsequent PA (PA → mood: posterior probability = 99.9%; mood → PA: 89.6%). In SSD, these within-day couplings were attenuated and more heterogeneous across individuals (PA → mood: 87.2%; mood → PA: 67.5%).

PA and mood are dynamically and bidirectionally linked within short temporal windows throughout the day, with stronger coupling in HC than in SSD. The substantial individual variability observed in SSD highlights the need for personalized, sensor-informed interventions, as aggregated analyses may obscure clinically relevant microtemporal dynamics.

Childhood trauma increases the risk for psychosis. However, mechanisms underlying this relationship remain unclear. In the present study, we aimed to investigate whether negative cognitive self-schemas or basic self-disturbance mediate the association between childhood trauma subtypes (abuse and neglect) and psychotic experiences in patients with psychotic disorders, unaffected siblings, and healthy controls.

This study was performed in a subsample of 110 patients, 120 siblings, and 48 healthy controls within the Genetic Risk and Outcome of Psychosis (GROUP) study. Parallel mediation models were used while correcting for age and sex. Abuse and neglect were measured with the childhood trauma questionnaire (CTQ), psychotic experiences with the Community Assessment of Psychic Experiences (CAPE), self-disturbance phenomena with the Self-Experience Lifetime Frequency Scale (SELF), and negative self-schemas with the Brief Core Schemas Scale (BCSS).

In patients, negative self-schemas fully mediated the association between abuse and psychotic experiences, whereas self-disturbance partly mediated the effect of neglect on psychotic experiences. In siblings, both negative self-schemas and self-disturbance fully mediated the association between abuse/neglect and psychotic experiences. In controls, only negative self-schemas mediated the association between abuse and neglect and psychotic experiences.

Our findings suggest that negative self-schemas and self-disturbance phenomena are relevant factors in explaining the association between childhood trauma and psychotic experiences across illness liability groups. Further research is needed to explore the causal pathways between trauma, negative self-schemas, self-disturbance phenomena, and psychotic experiences.

Although recent stressful life events (SLEs) have been implicated as a risk factor for schizophrenia, prospective evidence and the underlying immunometabolic mechanisms remain unclear. This study investigated the association between recent SLEs and schizophrenia risk and explored the potential mediating role of immunometabolic markers.

Data were drawn from the UK Biobank prospective cohort. Cox proportional hazards models, linear mixed-effects models, and mediation analyses were applied.

We included 493,294 participants (mean [SD] age, 56.55 [8.09] years; 45.56% male) free of schizophrenia at baseline, among whom 492 schizophrenia cases were identified over a median follow-up of 13.91 years. Exposure to any recent SLEs was associated with a 49% higher risk of schizophrenia, with evidence of a dose–response relationship (e.g. highest SLE score group: HR, 7.15, 95% CI: 5.74–9.11). Among individual SLE types, ‘serious illness, injury, or assault to yourself’ and ‘financial difficulties’ showed the strongest associations with schizophrenia risk. Recent SLEs were associated with alterations in multiple immunometabolic markers, several of which were also significantly associated with schizophrenia risk. Mediation analyses indicated that specific immunometabolic markers (e.g. C-reactive protein and degree of unsaturation) partially mediated the association between recent SLEs and subsequent schizophrenia risk, with proportion mediated estimates of 9.0% and 11.7%, respectively.

In this cohort of predominantly middle-aged and older adults, recent SLEs were associated with increased subsequent schizophrenia risk. Immunometabolic pathways may partially underlie this association, supporting further investigation of integrated psychosocial and biological approaches to schizophrenia risk stratification.

Artificial intelligence (AI) is explored as a tool to expand access to mental health care, offering support and self-disclosure. To date, the perspectives of individuals with schizophrenia on AI have been absent from the medical literature. This exploratory study represents an effort to report schizophrenia patient perspectives on AI chatbots through a focus group.

We conducted an exploratory, cross-sectional, qualitative study using semi-structured focus groups at a large academic health center in New York City. Participants included patients with schizophrenia spectrum disorders (n = 17), caregivers (n = 7), and providers (n = 4). Discussions explored familiarity with AI, privacy, and perceived utility of a hypothetical AI companion tool. Transcripts were analyzed using thematic analysis.

Five themes were identified in this exploratory analysis: (1) ‘Available When No One Else Is’, reflecting participants’ recognition of AI as a potentially accessible source of between-visit support; (2) ‘What Would It Do?’ capturing uncertainty about AI’s role; (3) ‘How Can I Trust It?’ highlighting trust as the central barrier, shaped by privacy concerns and schizophrenia-specific vulnerabilities; (4) ‘A Tool, Not a Therapist’, emphasizing that AI was acceptable only as a supplement to, not a replacement for, human care; and (5) ‘Keep It Low-Risk’, describing the narrow range of acceptable use cases, limited to supportive functions.

These exploratory findings suggest that AI is perceived as both a promising adjunct to care and a potential risk in schizophrenia. Implementation should emphasize transparency, trust, and clear boundaries, with AI designed to support, not replace, existing care.

Charting developmental abnormalities in future psychotic disorder could help identify pathogenic processes. Head circumference (HC) is a proxy for brain size. Studies of HC and its developmental trajectories in future psychotic disorder cases and across the psychosis spectrum are inconclusive. It is important to determine whether deviations in HC are specific or reflect a general growth disorder.

We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC) to chart HC trajectories from birth to 15 years in individuals with operationally-defined psychotic disorder (N = 62), psychotic experiences (PE; N = 339) and controls (N = 3,527). We extracted total intracranial volume (TIV) from MRI data on a subset (N = 347) scanned at age 20.

HC at birth, 7 and 15 years was correlated with TIV at age 20 (r’s = 0.45–0.77). The psychotic disorder group had consistently smaller HC from birth through to adolescence (B = −0.26, p = 0.02). Females with PE had smaller HC across childhood and adolescence (B = −0.27, p = 0.016), while males with PE had consistently larger HC (B = 0.35, p = 0.023). The effects did not change when controlling for maternal and pregnancy-related characteristics but attenuated after controlling for height, except in males with PE.

Deviations in head and hence brain size across the psychosis spectrum likely reflect a more general growth abnormality. Future studies should examine factors regulating growth during early life in relation to psychosis, including sex, to understand underlying mechanisms.

The influential predictive processing theory of psychosis posits that symptoms arise from an imbalanced weighting of predictions (priors) and sensory evidence, yet studies present conflicting results. This is particularly problematic as findings from single tasks with modest sample sizes are frequently used to advance a theory for a generalized altered reliance on priors in psychosis.

This study presents a random-effects, multi-level meta-analysis (PROSPERO CRD42024574379) evaluating evidence for aberrant reliance on priors in psychosis across perceptual tasks. The search identified articles in Embase, MEDLINE, APA PsycINFO, and APA PsycArticles published between 1st January 2005—31st October 2024, with risk of bias assessed using the Newcastle-Ottawa Scale. Included articles (34 results from 27 studies) compared adults with schizophrenia-spectrum psychosis (SZ; n = 904) to healthy controls (n = 1,039) on behavioral measures representing reliance on priors.

Results provided no evidence for atypical reliance on priors in psychosis (g = .03, 95% CI [−0.27, 0.34]; p = .818) or associations with delusions (6 results; SZ = 183; r = −.16, 95% CI [−0.51, 0.19]; p = .293) or hallucinations (10 results; SZ = 370; r = .04, 95% CI [−0.28, 0.36]; p = .780). In contrast with the theory that psychosis differentially affects priors at different levels of the cognitive hierarchy, a sub-group analysis indicated that a two-level hierarchical model of priors did not reconcile conflicting results (F (1,32) = 0.1, p = .758).

These findings do not suggest that psychosis is associated with a generalized predictive processing deficit spanning multiple aspects of perception.

Cannabis use is common in people with psychosis and is associated with poor outcomes.

To assess insight into cannabis use disorder, perceptions about how cannabis use influences mental health and the desire to change cannabis use among people with psychotic disorders.

Adults with a psychotic disorder and comorbid cannabis use disorder were asked about the impact of cannabis use on their illness and their desire to change cannabis use. Cannabis use disorder was determined using DSM-5 criteria. Participants completed the Substance Use Awareness and Insight Scale.

Sixty-two participants (mean age: 42 years; 95% male; 65% Black ethnicity; 85% severe DSM-5 cannabis use disorder) completed the study. The mean total Substance Use Awareness and Insight Scale score was 4.8 (s.d.: 2.1), with subdomain scores of 3.7 (s.d.: 2.8) for general illness awareness, 6.2 (s.d.: 2.8) for symptom attribution, 3.5 (s.d.: 2.9) for awareness of need for treatment and 5.6 (s.d.: 3.8) for awareness of negative consequences. Fifty-eight per cent said that cannabis use made their mental health ‘better’, whereas only 15% said it made it ‘worse’; 27% reported that cannabis made their psychotic symptoms ‘better’, and 21% said it made them ‘worse’. Nevertheless, more than half the sample (53%) said they wanted to reduce or stop using cannabis.

Many people with psychotic disorders have impaired insight into cannabis use disorder or believe that cannabis improves their mental health; these factors may contribute to persistent use in this population. Interventions that improve insight may help to address ambivalence and support those already wishing to reduce use.