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Abstract Aim To address the need for additional education in the management of mental illness in the critical care setting by providing a broad overview of the interrelationship between critical illness and mental illness. The paper also offers practical advice to support critical care staff in managing patients with mental illness in critical care by discussing two hypothetical case scenarios involving aggressive and disorganised behaviour. People living with mental illness are over‐represented among critically unwell patients and experience worse outcomes, contributing to a life expectancy up to 30 years shorter than their peers. Strategic documents call for these inequitable outcomes to be addressed. Staff working in intensive care units (ICUs) possess advanced knowledge and specialist skills in managing critical illness but have reported limited confidence in managing patients with comorbid mental illness. Design & Methods A discursive paper, drawing on clinical experience and research of the authors and current literature. Results Like all people, patients with mental illnesses draw on their cognitive, behavioural, social and spiritual resources to cope with their experiences during critical illness. However, they may have fewer resources available due to co‐morbid mental illness, a history of trauma and social disadvantage. By identifying and sensitively addressing patients' underlying needs in a trauma‐informed way, demonstrating respect and maximising patient autonomy, staff can reduce distress and disruptive behaviours and promote recovery. Caring for patients who are distressed and/or display challenging behaviours can evoke strong and unpleasant emotional responses. Self‐care is fundamental to maintaining a compassionate approach and effective clinical judgement. Staff should be enabled to accept and acknowledge emotional responses and access support—informally with peers and/or through formal mechanisms as needed. Organisational leadership and endorsement of the principles of equitable care are critical to creation of the environment needed to improve outcomes for staff and patients. Relevance to clinical practice ICU nurses hold an important role in the care of patients with critical illnesses and are ideally placed to empower, advocate for and comfort those patients also living with mental illness. To perform these tasks optimally and sustainably, health services have a responsibility to provide nursing staff with adequate education and training in the management of mental illnesses, and sufficient formal and informal support to maintain their own well‐being while providing this care. Patient and public involvement This paper is grounded in accounts of patients with mental illness and clinicians providing care to patients with mental illness in critical care settings but there was no direct patient or public contribution.

This document is a summary of the top papers published in the journal Brain Sciences in 2022-2023. The papers cover a range of topics in neuropsychology, including the brain correlates of social and emotional processes, stress-related pathologies, phobias, substance use disorders, and addiction. One paper focuses on the negative effects of excessive use of social networking sites, while another examines the toxic effects of synthetic cannabinoids. Other papers explore the brain correlates of eating disorders, stress measurement in daily life situations, the role of body memories in mental health problems, cognitive impairments in patients with psychotic episodes, the impact of the COVID-19 pandemic on clinical and research activities, and the study of normal and atypical development. Two papers specifically address developmental dyslexia, looking beyond the traditional phonological interpretation of the disorder. Overall, these papers contribute to a better understanding of various neuropsychological issues and offer insights for potential interventions and treatments. [Extracted from the article]

Sex differences exist in many neurological and psychiatric diseases, but these have not always been addressed adequately in research. In order to address this, it is necessary to consider how sex is incorporated into the design (e.g. using a balanced design) and into the analyses (e.g. using sex as a covariate) in the published literature. We surveyed papers published in 2009 and 2019 across six journals in neuroscience and psychiatry. In this sample, we find a 30% increase in the percentage of papers reporting studies that included both sexes in 2019 compared with 2009. Despite this increase, in 2019 only 19% of papers in the sample reported using an optimal design for discovery of possible sex differences, and only 5% of the papers reported studies that analysed sex as a discovery variable. We conclude that progress to date has not been sufficient to address the importance of sex differences in research for discovery and therapeutic potential for neurological and psychiatric disease. Sex differences occur in many neurological and psychiatric diseases, and yet research is not always designed optimally to identify these. Here the authors perform a study of how sex was incorporated into the design and analyses of papers published six journals in neuroscience and psychiatry in 2009 compared with 2019. [ABSTRACT FROM AUTHOR]

Background: The most impactful research on pediatric type 1 diabetes (T1D) remains unknown. Objective: We aimed to evaluate the characteristics and impact of the 100 most-cited articles on pediatric T1D. Materials and Methods: Using a predefined bibliometric strategy, the Scopus database was searched for high-cited papers (HCPs) published from 2001 to 2020. Articles were evaluated for data on the publication year, countries, authors, journals, topics, and types. Social network analysis was performed to visualize the interaction among countries, organizations, and authors using VOSviewer software. Results: The top 100 HCPs received 390 to 4634 citations, averaging 773.5 citations per paper (CPP). The funded HCPs (n = 50) had a higher impact (CPP 791.5). The majority of HCPs (n = 83) were collaborative. Classifying by research type, 65 studies were clinical (n = 65), risk factors (n = 27), epidemiology (n = 26), pathophysiology (n = 16), treatment outcome (n = 13), genetics (n = 12), complications (n = 3), quality of life (n = 2), and prognosis (n = 1). The number of authors involved was 1,101, affiliated with 545 organizations in 27 countries; the USA (n = 64) and the UK (n = 24) were the most productive countries, whereas Australia and the UK were the most impactful. D.M. Nathan and J.M. Lawrence were the most prolific authors, while P. Raskin and J.M. Lachin were the most impactful. Conclusions: High-income countries such as the USA, UK, and Australia contribute significantly to high-impact pediatric T1D research. Funding and collaboration improve the impact of citations in publications. Less researched areas such as treatment outcomes, genetics, complications, quality of life, and prognosis should be the focus of future research on pediatric T1D. [ABSTRACT FROM AUTHOR]

The problem of mental health in academia is increasingly discussed in literature, and to extract meaningful insights from the growing amount of scientific publications, text mining approaches are used. In this study, BERTopic, an advanced method of topic modeling, was applied to abstracts of 2,846 PubMed articles on depression, anxiety, and burnout in academia published in years 1975-2023. BERTopic is a modular technique comprising a text embedding method, a dimensionality reduction procedure, a clustering algorithm, and a weighing scheme for topic representation. A model was selected based on the proportion of outliers, the topic interpretability considerations, topic coherence and topic diversity metrics, and the inevitable subjectivity of the criteria was discussed. The selected model with 27 topics was explored and visualized. The topics evolved differently with time: research papers on students' pandemic-related anxiety and medical residents' burnout peaked in recent years, while publications on psychometric research or internet-related problems are yet to be presented more amply. The study demonstrates the use of BERTopic for analyzing literature on mental health in academia and sheds light on areas in the field to be addressed by further research. [ABSTRACT FROM AUTHOR]

Aim: To address the need for additional education in the management of mental illness in the critical care setting by providing a broad overview of the interrelationship between critical illness and mental illness. The paper also offers practical advice to support critical care staff in managing patients with mental illness in critical care by discussing two hypothetical case scenarios involving aggressive and disorganised behaviour. People living with mental illness are over‐represented among critically unwell patients and experience worse outcomes, contributing to a life expectancy up to 30 years shorter than their peers. Strategic documents call for these inequitable outcomes to be addressed. Staff working in intensive care units (ICUs) possess advanced knowledge and specialist skills in managing critical illness but have reported limited confidence in managing patients with comorbid mental illness. Design & Methods: A discursive paper, drawing on clinical experience and research of the authors and current literature. Results: Like all people, patients with mental illnesses draw on their cognitive, behavioural, social and spiritual resources to cope with their experiences during critical illness. However, they may have fewer resources available due to co‐morbid mental illness, a history of trauma and social disadvantage. By identifying and sensitively addressing patients' underlying needs in a trauma‐informed way, demonstrating respect and maximising patient autonomy, staff can reduce distress and disruptive behaviours and promote recovery. Caring for patients who are distressed and/or display challenging behaviours can evoke strong and unpleasant emotional responses. Self‐care is fundamental to maintaining a compassionate approach and effective clinical judgement. Staff should be enabled to accept and acknowledge emotional responses and access support—informally with peers and/or through formal mechanisms as needed. Organisational leadership and endorsement of the principles of equitable care are critical to creation of the environment needed to improve outcomes for staff and patients. Relevance to clinical practice: ICU nurses hold an important role in the care of patients with critical illnesses and are ideally placed to empower, advocate for and comfort those patients also living with mental illness. To perform these tasks optimally and sustainably, health services have a responsibility to provide nursing staff with adequate education and training in the management of mental illnesses, and sufficient formal and informal support to maintain their own well‐being while providing this care. Patient and public involvement: This paper is grounded in accounts of patients with mental illness and clinicians providing care to patients with mental illness in critical care settings but there was no direct patient or public contribution. [ABSTRACT FROM AUTHOR]

Background and Hypothesis Processing speed dysfunction is a core feature of psychosis and predictive of conversion in individuals at clinical high risk (CHR) for psychosis. Although traditionally measured with pen-and-paper tasks, computerized digit symbol tasks are needed to meet the increasing demand for remote assessments. Therefore we: (1) assessed the relationship between traditional and computerized processing speed measurements; (2) compared effect sizes of impairment for progressive and persistent subgroups of CHR individuals on these tasks; and (3) explored causes contributing to task performance differences. Study Design Participants included 92 CHR individuals and 60 healthy controls who completed clinical interviews, the Brief Assessment of Cognition in Schizophrenia Symbol Coding test, the computerized TestMyBrain Digit Symbol Matching Test, a finger-tapping task, and a self-reported motor abilities measure. Correlations, Hedges' g, and linear models were utilized, respectively, to achieve the above aims. Study Results Task performance was strongly correlated (r  = 0.505). A similar degree of impairment was seen between progressive (g  = −0.541) and persistent (g  = −0.417) groups on the paper version. The computerized task uniquely identified impairment for progressive individuals (g  = −477), as the persistent group performed similarly to controls (g  = −0.184). Motor abilities were related to the computerized version, but the paper version was more related to symptoms and psychosis risk level. Conclusions The paper symbol coding task measures impairment throughout the CHR state, while the computerized version only identifies impairment in those with worsening symptomatology. These results may be reflective of sensitivity differences, an artifact of existing subgroups, or evidence of mechanistic differences. [ABSTRACT FROM AUTHOR]

Objective: Despite the frequent comorbidity of substance use disorders (SUDs) and psychiatric disorders, it remains unclear if screening for substance use in behavioral health clinics is a common practice. The aim of this review is to examine what is known about systematic screening for substance use in outpatient behavioral health clinics. Methods: We conducted a PRISMA-based systematic literature search assessing substance use screening in outpatient adult and pediatric behavioral health settings in PubMed, Embase, and PsycINFO. Quantitative studies published in English before May 22, 2020 that reported the percentage of patients who completed screening were included. Results: Only eight articles met our inclusion and exclusion criteria. Reported prevalence of screening ranged from 48 to 100%, with half of the studies successfully screening more than 75% of their patient population. There were limited data on patient demographics for individuals who were and were not screened (e.g., gender, race) and screening practices (e.g., electronic versus paper/pencil administration). Conclusions: The results of this systematic review suggest that successful screening for substance use in behavioral health settings is possible, yet it remains unclear how frequently screening occurs. Given the high rates of comorbid SUD and psychopathology, future research is necessary regarding patient and clinic-level variables that may impact the successful implementation of substance use screening. Trial registry A methodological protocol was registered with the PROSPERO systematic review protocol registry (ID: CRD42020188645). [ABSTRACT FROM AUTHOR]

Cognitive deficits are a core feature of mental and behavioral disorders, leading to poor treatment adherence and functionality. Virtual reality (VR) methodologies are promising solutions for cognitive interventions in psychiatry once they provide greater ecological validity. This study assessed and compared two content-equivalent cognitive training (CT) interventions, delivered in desktop VR (Reh@City v2.0) and paper-and-pencil (Task Generator (TG)) formats, in patients with mental and behavioral disorders. 30 patients were randomly assigned to the Reh@City v2.0 group and the TG group. Both groups of patients underwent a time-matched 24-sessions intervention. Neuropsychological assessments were performed at baseline, post-intervention, and follow-up. A within-groups analysis revealed significant improvements in visual memory and depressive symptomatology after the Reh@City intervention. The TG group improved in processing speed, verbal memory, and quality of life (social relationships and environmental domains). Between groups, Reh@City led to a greater reduction in depressive symptomatology, whereas the TG group showed higher improvements in social relationships aspects of quality of life. At follow-up, previous gains were maintained and new improvements found in the Reh@City (global cognitive function, language, visuospatial and executive functions) and the TG groups (attention). The Reh@City significantly reduced depressive symptomatology, and the TG led to greater improvements in processing speed, abstraction, and social relationships domain of quality of life at follow-up. Both interventions were associated with important cognitive, emotional, and quality of life benefits, which were maintained after two months. Reh@City and TG should be considered as complementary CT methods for patients with mental and behavioral disorders. Trial registration The trial is registered at ClinicalTrials.gov, number NCT04291586. [ABSTRACT FROM AUTHOR]

Objective: The number of citations to a paper represents the weight of that work in a particular area of interest. Several highly cited papers are listed in the bibliometric analysis. This study aimed to identify and analyze the 100 most cited papers in insomnia research that might appeal to researchers and clinicians. Methods:We reviewed theWeb of Science (WOS) Core Collection database to identify articles from 1985 to 24 March 2022. The R bibliometric package was used to further analyze citation counts, authors, year of publication, source journal, geographical origin, subject, article type, and level of evidence. Word co-occurrence in 100 articles was visualized using VOS viewer software. Results: A total of 44,654 manuscripts were searched on the Web of Science. Between 2001 and 2021, the top 100 influential manuscripts were published, with a total citation frequency of 38,463. The top countries and institutions contributing to the field were the U.S. and Duke University. Morin C.M. was the most productive author, ranking first in citations. Sleep had the highest number of manuscripts published in the top 100 (n = 31), followed by Sleep Medicine Reviews (n = 9). The most cited manuscript (Bastien et al., Sleep Medicine, 2001; 3,384 citations) reported clinical validation of the Insomnia Severity Index (ISI) as a brief screening indicator for insomnia and as an outcome indicator for treatment studies. Co-occurrence analyses suggest that psychiatric disorders combined with insomnia and cognitive behavioral therapy remain future research trends. Conclusion: This study provides a detailed list of the most cited articles on insomnia. The analysis provides researchers and clinicians with a detailed overview of the most cited papers on insomnia over the past two decades. Notably, COVID-19, anxiety, depression, CBT, and sleep microstructure are potential areas of focus for future research. [ABSTRACT FROM AUTHOR]

Acute and chronic operational and organizational stress dysregulate associated neuroendocrine responses and have been shown to increase health risks and occupational performance errors. The current paper addressed a research gap comparing biopsychosocial stress measures and its interaction on police lethal force errors during realistic critical incident (CI) scenarios. A combined sample (n=101) of frontline and tactical police officers completed externally valid, live-action scenarios requiring lethal force decisions paralleling field experiences. Officers provided diurnal and reactive (pre-to-post CI) salivary cortisol samples, and self-reported mental health and stress symptoms. Higher cortisol awakening response significantly predicted increased odds of committing a lethal force error by 6.45%. However, further regressions deemphasizing influential outliers via logarithmic transformations (as per Psychoneuroendocrinology expert consensus guidelines⁠—Stalder et al., 2016) revealed a no longer significant association. Reactive cortisol did not predict lethal force error across all subsamples. Consistent with previous literature, several significant relationships between mental health symptoms and cortisol were observed. The use of cortisol in field research presents a number of collection, control, and cost challenges. Furthermore, outliers are necessary to include in analyses to represent the potential cortisol dysregulation in police officers deemed 'fit for duty' by their respective organizations. The current findings suggest that diurnal cortisol is heavily influenced by outlier values, and reactive cortisol may be too subtle to predict lethal force errors during acute stress. While purposeful for predicting long-term health outcomes, pursuit of alternative measures may be more suitable for assessing acute operational errors in field research. [ABSTRACT FROM AUTHOR]

Around a third of the total population of Europe suffers from mental disorders. The use of electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose mental disorders has recently been shown to be a prominent research area, as exposed by several reviews focused on the field. Nevertheless, previous to the application of ML algorithms, EEG data should be correctly preprocessed and prepared via Feature Engineering (FE). In fact, the choice of FE techniques can make the difference between an unusable ML model and a simple, effective model. In other words, it can be said that FE is crucial, especially when using complex, non-stationary data such as EEG. To this aim, in this paper we present a Systematic Mapping Study (SMS) focused on FE from EEG data used to identify mental disorders. Our SMS covers more than 900 papers, making it one of the most comprehensive to date, to the best of our knowledge. We gathered the mental disorder addressed, all the FE techniques used, and the Artificial Intelligence (AI) algorithm applied for classification from each paper. Our main contributions are: (i) we offer a starting point for new researchers on these topics, (ii) we extract the most used FE techniques to classify mental disorders, (iii) we show several graphical distributions of all used techniques, and (iv) we provide critical conclusions for detecting mental disorders. To provide a better overview of existing techniques, the FE process is divided into three parts: (i) signal transformation, (ii) feature extraction, and (iii) feature selection. Moreover, we classify and analyze the distribution of existing papers according to the mental disorder they treat, the FE processes used, and the ML techniques applied. As a result, we provide a valuable reference for the scientific community to identify which techniques have been proven and tested and where the gaps are located in the current state of the art. [ABSTRACT FROM AUTHOR]

A large proportion of the world's disease burden is attributable to mental illnesses. Although effective interventions are available, many patients still have limited access to evidence-based treatments. Aside from access, treatment gaps, including inappropriate medication selection and monitoring, are also routinely recognised. Mental health clinical pharmacists can help address these gaps and enable patients to receive optimised pharmaceutical care, particularly appropriate medication selection and monitoring. The European Society of Clinical Pharmacy (ESCP) Special Interest Group on Mental Health was established to improve standardised service provision in mental health settings across Europe. The Special Interest Group identified significant barriers (predominantly associated with reimbursement and position within the multidisciplinary team) to effective pharmaceutical care amongst those with mental illnesses. This commentary presents recommendations to address these gaps through improved mental health clinical pharmacy service provision. [ABSTRACT FROM AUTHOR]

The mental well-being of a person is their mental state. Chemical abnormalities in the brain cause mental health problems. It is important to monitor the mental health of different groups in order to predict health-related disorders. The community consists of working professionals and college students. It is widely believed that stress and grief affect people of all ages and backgrounds. Some serious mental health disorders, such as anxiety, bipolar disorder, and schizophrenia, often evolve and produce symptoms that can be recognized early. Such mental disorders could be avoided more successfully if abnormal mental states are detected in the early stages of the disease, allowing for additional care and treatment. This study analyzed the accuracy of four data mining techniques and introduced a new ensemble technique to improve their accuracy in identifying mental health issues. The data mining techniques are Logistic Regression, KNN Classifier, Decision Tree Classifier, and Random Forest. This paper provides scope for other researchers and practitioners seeking to achieve higher accuracy in identifying mental health issues using enhanced data mining algorithms to meet several accuracy criteria. [ABSTRACT FROM AUTHOR]

Objectives: The present study examines the publication output of authors from India on the topic of "Covid-19 and Psychology" using bibliometric methods. Methods: The publications as listed in Scopus database were identified by using "Covid-19" and its synonyms keywords in "Keyword" and "Title" tags. The results obtained were further restricted to subject "Psychology" under subject tag and country to India. Results: 372 publications emerged in the last 2 years on the topic of "Covid-19 and Psychology", accounting to 4.63% share of global output. About 9.95% and 25.81% share of Indian publications received external funding support and involved international collaboration, respectively. The author's from 277 organizations and 416 authors participated unevenly in the research in this area. The highest number of publications emerged from National Institute of Mental Health and Allied Sciences (NIMHANS), Bangaluru, followed by Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, All India Institute of Medical Sciences (AIIMS), New Delhi and All India Institute of Medical Sciences (AIIMS), Bhubaneswar. Authors from Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Pondicherry had highest impact in terms of citations per paper and relative citation index, followed by authors from King George's Medical University (KGMU), Lucknow, and Manipal Academy of Higher Education (MAHE), Manipal. The journals that published the highest number of publications were Asian Journal of Psychiatry (158 papers), Indian Journal of Psychological Medicine (44 papers), and Frontier in Psychology (18 papers). The most commonly investigated topics in terms of frequency of appearances of keywords were mental health (93), followed by anxiety (80), mental disease (68), depression (64), mental stress (34), and social isolation. Conclusion: Over the last 2 years a large number of publications have emerged in the area of COVID-19 and psychology from India. [ABSTRACT FROM AUTHOR]

This special issue brings together five original research papers on chronic conditions in South Asian contexts with a view to rethink dominant discourses of risk, evidence and control surrounding the category of chronic conditions. Focusing on the multiple and contradictory (re)definitions of what counts as illness, specifically in the context of the rising burden of chronic illness, the papers in this issue deal with a range of health care practices from individual patients negotiating with 'healthy diet', to policy questions about the etiology of emerging disease burden and appropriateness of pharmaceutical interventions in 'traditional' sites of healing. While some of the chronic illnesses addressed in this special issue have received considerable attention from anthropologists (e.g. mental illness, diabetes), others, like leucorrhea have rarely been studied by anthropologists, despite the growing literature on 'chronic illnesses'. [ABSTRACT FROM AUTHOR]

Aim: To synthesize suicide prevention strategies for older adults. The review question was Which suicide prevention strategies are useful for older adults? Design: Integrative review. Data sources: Academic Search Premier, CINAHL, Ovid PsycINFO and PubMed were searched for articles published between January 2009 and December 2019. Review methods: An integrative review of quantitative, qualitative and theoretical papers with a qualitative thematic analysis. Results: Key aspects of the included studies contributed to the formulation of four themes: (1) Recognizing older adults' physical and/or mental health problems and referring them for help and treatment, (2) Designing an educational programme, (3) Communication and dialogue about warning signs and (4) Social support and awareness of causing significant others emotional pain. The findings indicate an urgent need to identify effective suicide prevention strategies for older adults. [ABSTRACT FROM AUTHOR]

Recently, there will be more than 4.62 billion social media users worldwide. A large number of users tend to publish personal emotional dynamics or express opinions on social media. These massive user data provide data support for the development of mental illness detection research and have achieved good results. However, it is difficult for current mental illness detection models to accurately identify key emotional features from a large number of posts issued by users to detect problem users. In view of the fact that the existing models cannot more accurately extract the words with high emotional contribution in the content of user posts, this paper proposes two hierarchical user post feature representation models, named Single-Gated LeakReLU-CNN (SGL-CNN) and Multi-Gated LeakyReLU-CNN (MGL-CNN). We leverage these 2 models to identify users with mental illness in online forums. For all posts published by each user within a certain time span, the model proposed in this paper can identify key emotional features in them and filter out other unimportant information as much as possible. In addition, the addition of gating units in this paper can significantly improve the performance of emotion detection tasks. The experimental results based on the task of RSDD dataset prove that the performance of the model proposed in this paper is superior to that of the existing methods. [ABSTRACT FROM AUTHOR]

Recent developments in the fields of natural language processing (NLP) and machine learning (ML) have shown significant improvements in automatic text processing. At the same time, the expression of human language plays a central role in the detection of mental health problems. Whereas spoken language is implicitly assessed during interviews with patients, written language can also provide interesting insights to clinical professionals. Existing work in the field often investigates mental health problems such as depression or anxiety. However, there is also work investigating how the diagnostics of eating disorders can benefit from these novel technologies. In this paper, we present a systematic overview of the latest research in this field. Our investigation encompasses four key areas: (a) an analysis of the metadata from published papers, (b) an examination of the sizes and specific topics of the datasets employed, (c) a review of the application of machine learning techniques in detecting eating disorders from text, and finally (d) an evaluation of the models used, focusing on their performance, limitations, and the potential risks associated with current methodologies. [ABSTRACT FROM AUTHOR]