Introduction Initial COVID‐19 restrictions forced changes in the contexts (e.g., with who and where) within which individuals consumed alcohol. We aimed to explore different profiles of drinking contexts during initial COVID‐19 restrictions and their association with alcohol consumption. Method We used latent class analysis (LCA) to explore subgroups of drinking contexts among 4891 respondents of the Global Drug Survey from the United Kingdom, New Zealand and Australia who reported drinking alcohol in the month prior to data collection (3 May–21 June 2020). Ten binary LCA indicator variables were generated from a survey question about last month alcohol settings. Negative binomial regression was used to explore the association between the latent classes and respondents' total number of drinks consumed in the last 30 days (i.e., alcohol consumption). Results The LCA found six distinct classes of individuals who reported drinking in the following contexts: household (36.0%); alone (32.3%); alone and household (17.9%); gatherings and household (9.5%); party (3.2%); and everywhere (1.1%), with the last group associated with the highest probability of increased alcohol consumption during this time. Male respondents and those aged 35 or older were most likely to report increased alcohol consumption. Discussion and Conclusions Our findings suggest that drinking contexts, sex and age influenced alcohol consumption during the early stages of the COVID‐19 pandemic. These findings highlight a need for improved policy targeting risky drinking in home settings. Further research should explore whether COVID‐19‐induced shifts in alcohol use persist as restrictions are lifted.

ObjectivesEarly identification of inpatients at risk of developing delirium and implementing preventive measures could avoid up to 40% of delirium cases. Machine learning (ML)-based prediction models may enable risk stratification and targeted intervention, but establishing their current evolutionary status requires a scoping review of recent literature.MethodsWe searched ten databases up to June 2022 for studies of ML-based delirium prediction models. Eligible criteria comprised: use of at least one ML prediction method in an adult hospital inpatient population; published in English; reporting at least one performance measure (area under receiver-operator curve (AUROC), sensitivity, specificity, positive or negative predictive value). Included models were categorised by their stage of maturation and assessed for performance, utility and user acceptance in clinical practice.ResultsAmong 921 screened studies, 39 met eligibility criteria. In-silico performance was consistently high (median AUROC: 0.85); however, only six articles (15.4%) reported external validation, revealing degraded performance (median AUROC: 0.75). Three studies (7.7%) of models deployed within clinical workflows reported high accuracy (median AUROC: 0.92) and high user acceptance.DiscussionML models have potential to identify inpatients at risk of developing delirium before symptom onset. However, few models were externally validated and even fewer underwent prospective evaluation in clinical settings.ConclusionThis review confirms a rapidly growing body of research into using ML for predicting delirium risk in hospital settings. Our findings offer insights for both developers and clinicians into strengths and limitations of current ML delirium prediction applications aiming to support but not usurp clinician decision-making.

Kurzfassung In vielen Unternehmen steigt der Anspruch an die Effizienz der unternehmenseigenen Prozesse, um aus den vorhandenen Kapazitäten den maximalen Nutzen herauszuholen. Dazu gehört u. a. das Vermeiden von Verschwendungen innerbetrieblicher Kapazitäten. Mit der Optimierung von Arbeitsplänen durch Anpassung des Detaillierungsgrads an die Bedürfnisse der Produktion kann bereits bei der Arbeitsvorbereitung ein wesentlicher Beitrag dazu geleistet werden. Eine Verbesserung der Informationsbereitstellung aufgrund eines optimal detaillierten Arbeitsplans kann sich positiv auf die Effizienz eines Unternehmens auswirken, indem nicht wertschöpfende Tätigkeiten reduziert werden.

In der Energiewende werden dringend Fortschritte benötigt – doch immer wieder kommt es bei den erneuerbaren Energien zu Akzeptanzproblemen und Klageverfahren. Im Projekt „WindGISKI“ soll ein Geoinformationssystem auf Basis Künstlicher Intelligenz entwickelt werden, das an diesen Fragestellungen ansetzt. In einem Vorprojekt wurden dazu bereits Einflussfaktoren innerhalb des Spannungsfeldes aus Arten-, Umwelt- und Klimaschutz identifiziert. Ein interdisziplinäres Team aus Wissenschaft und Wirtschaft geht mit der Entwicklung der Künstlichen Intelligenz nun den nächsten Schritt.

KEYWORDSperson and family centered care, life course, quality of life, diseases (MeSH), psychometric, continuum of care, performance measurement quality of care Editorial on the Research Topic Innovations in the mental health applications of interRAI assessments What is interRAI?Mental health issues concern individuals and populations in all stages of life and pose unique challenges to healthcare systems. People with mental health conditions are exposed to complex interactions between psychological, biological, social, and environmental influences that are unlikely to be mitigated by one-dimensional assessment and screening systems. The interRAI research collaborative-www.interrai.org-aims to improve the quality of life of people of all ages, particularly those who are vulnerable due to some combination of age-related or developmental health problems, disability, medical complexity, or mental health challenges. The collaborative does this by designing and implementing comprehensive systems that cross the continuum of health and social care settings. Since Morris et al.(1) first described the deployment of a single-sector Resident Assessment Instrument (RAI) for geriatrics in response to the US Omnibus Reconciliation Act of 1987, interRAI instruments have evolved to become a fully integrated suite of measures spanning populations of all ages. Researchers and health professionals use interRAI systems in more than 35 countries for care planning, outcome measurement, resource allocation, quality improvement, and policy development.This Frontiers in Psychiatry special issue presents a compilation of research to illustrate the novel mental health (MH) applications of the interRAI suite in psychiatric and non-mental healthcare settings. Today, more than a billion people experience mental health disorders, accounting for 19% of years lived with disability (2). While persons living with mental health disorders or addictions have diverse needs throughout their lives, healthcare services are frequently uncoordinated; often failing to holistically meet the needs of community. InterRAI instruments provide a highly validated mental health Frontiers in Psychiatry frontiersin.org