Artificial Intelligence (AI) assists recruiting and job searching. Such systems can be biased against certain characteristics. This results in potential misrepresentations and consequent inequalities related to people with mental health disorders. Hence occupational and mental health bias in existing Natural Language Processing (NLP) models used in recruiting and job hunting must be assessed. We examined occupational bias against mental health disorders in NLP models through relationships between occupations, employability, and psychiatric diagnoses. We investigated Word2Vec and GloVe embedding algorithms through analogy questions and graphical representation of cosine similarities. Word2Vec embeddings exhibit minor bias against mental health disorders when asked analogies regarding employability attributes and no evidence of bias when asked analogies regarding high earning jobs. GloVe embeddings view common mental health disorders such as depression less healthy and less employable than severe mental health disorders and most physical health conditions. Overall, physical, and psychiatric disorders are seen as similarly healthy and employable. Both algorithms appear to be safe for use in downstream task without major repercussions. Further research is needed to confirm this. This project was funded by the London Interdisciplinary Social Science Doctoral Training Programme (LISS-DTP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Background Schizophrenia spectrum disorders are associated with higher risk of unemployment for the individual. Antipsychotic usage is linked to less risk of relapse, fewer hospitalisations and lower rates of mortality. This study investigates the use and discontinuation of antipsychotics in individuals with psychosis in relation to employment and other functional outcomes. Methods Using Danish nationwide registers, we identify individuals aged ≥18 with first-time nonaffective psychosis diagnosis (ICD-10 F20-F29) during 1995-2024. Antipsychotic exposure is categorised as continuous use (≥80% coverage), intermittent use (20-80%), sustained discontinuation (<20%), or no use. Functional outcomes include: employment status, benefit code, hospital readmission. Within-subject analyses use each individual as their own control, with time-varying covariates and 6-, 12-, and 24-month lagged effects. Models adjust for treatment sequence, time since diagnosis, and concurrent psychotropic medication use. Hypotheses For individuals with schizophrenia spectrum disorders, periods of antipsychotic treatment will be associated with higher likelihood of employment/ education/training and independent living, relative to periods without such treatment. Exploratory hypotheses Periods of long acting injectables or clozapine treatment will be associated with higher likelihood of employment/ education and independent living relative to periods with other antipsychotic treatment.

Physical restraints are widely used in intensive care units (ICUs) despite uncertain clinical benefit and risks. We aimed to characterise patterns of restraint use, demographic and clinical predictors, and temporal trends before and after introduction of federal restraint-related reporting requirements. We conducted a retrospective cross-sectional study of 51,838 adults admitted to ICUs at Beth Israel Deaconess Medical Center, Boston, MA, USA, between 2008 and 2022, using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) electronic health record repository. Primary outcome was the proportion of ICU days with documented physical restraint use. Associations between restraint use and demographic and clinical factors were estimated using a binomial generalised linear model with a logit link. Propensity score matching compared Black and White patients under varying adjustment specifications. Among 51,838 patients (mean age 63.8 years; 57% male), 21,091 (40.7%) experienced restraint. Use increased from 36.9% in 2008–10 to 44.0% in 2020–22 (p < 0.0001). Asian (aOR 0.84, 95% CI 0.79–0.89) and Hispanic/Latino patients (aOR 0.87, 95% CI 0.83–0.92) had lower odds of restraint than White patients. Propensity score matching between Black and White patients revealed ethnic patterns were highly sensitive to model specification: excluding demographic characteristics revealed significant disparities, which were attenuated when psychiatric diagnoses were also excluded. Matched White patients were not representative of all White ICU patients but rather a subset resembling Black patients on observed characteristics. Restraint practices appear to vary with patient acuity, institutional factors, and communication barriers. The sensitivity of ethnic disparities to psychiatric diagnosis adjustment suggests these diagnoses may function as mediators rather than confounders, potentially reflecting systematic differences in clinical assessment along the causal pathway between ethnicity and restraint decisions. The non-representativeness of matched cohorts underscores that disparities depend on which patient subgroups are compared. Prospective multisite studies with standardized assessment protocols are needed to validate findings, disentangle true clinical variation from systematic bias and provide a more comprehensive understanding of restraint practices across US ICU settings. No study-specific funding was received.

Background: Antipsychotic medications are recommended for schizophrenia spectrum disorders, yet their long-term effects on functional recovery remain unclear, with conflicting evidence often derived from between-subject comparisons vulnerable to confounding by indication. Methods: We conducted a nationwide register-based cohort study of 65,630 individuals with incident schizophrenia spectrum disorders in Denmark (1998–2023). We modeled antipsychotic exposure against ‘productive engagement’ (employment or education). We used two analytical approaches: (1) within-subject stratified Cox models with time-varying covariates to eliminate time-invariant confounding; and (2) Fine–Gray competing risks models with baseline exposure, accounting for mortality and emigration. Results: Over 26.9 million person-weeks, the overall productive engagement rate was 48.2%. Integration of hospital pharmacy data revealed 6.1% exposure misclassification in studies relying solely on community records. The primary within-subject analysis revealed significant temporal heterogeneity: medication use was associated with reduced engagement rates in the acute (0–2 years; HR = 0.908) and consolidation phases (2–5 years; HR = 0.946), but reversed to a small positive association in the maintenance phase (5+ years; HR = 1.019). The between-subject Fine–Gray model, which estimates cumulative engagement probabilities, yielded an SHR of 1.002 (95% CI = 0.988–1.015), a population-level average that obscured these phase-specific dynamics. Conclusions: Antipsychotic pharmacotherapy exerts a time-dependent, biphasic impact on vocational recovery. We identified a window of vulnerability during the post-acute ‘consolidation’ phase (years 2–5) where treatment is associated with a transient reduction in productive engagement, before becoming protective after 5 years. These findings challenge the assumption that symptomatic stability automatically facilitates functional reintegration.

We will develop a novel approach to drug repurposing, utilising Natural Language Processing (NLP) and Literature Based Discovery (LBD) techniques. This will present a simplified, accessible drug repurposing pipeline using Word2Vec embeddings trained on PubMed abstracts to identify potential new medications to be repurposed. We present this approach in the context of antipsychotics, but it could be repeated for any available medication. The research is structured in three stages: Identification of candidate medications using Word2Vec algorithm trained on scientific literature. Empirical testing of identified candidates using a large hospital dataset to explore protective effects against disease onset. Validation of findings using a second, independent dataset to assess generalizability. This method addresses limitations in current machine learning-based drug repurposing approaches, including lack of external validation and limited accessibility. By leveraging Word2Vec’s ability to capture semantic relationships between words, the study aims to uncover hidden connections in medical literature that may lead to novel therapeutic discoveries. The protocol emphasizes transparency and reproducibility, utilizing publicly available electronic health record (EHR) databases for validation. This approach allows for tangible results even for researchers with limited machine learning expertise, bridging the gap between biomedical and information systems communities.