Search

New Orleans is no stranger to trauma. The Crescent City has a vast history of environmental calamities and oppression. Yet, New Orleans is renowned for its "joie de vivre"—or "love of life." Specifically, this community is known for its unique practice of second-line parades. Researchers have noted the healing power of second-line processions, but none have analyzed the practice and psychology of this ritual through a trauma-informed lens. The aim of this conceptual paper is to begin the conversation, rather than deliver hard fast conclusions, on the potential therapeutic function of second-line parades in response to grief. Relevant literature is presented to illustrate second-line parades, trauma theory, and to provide evidence that the therapeutic effects of second-lining may, in part, be explained by trauma theory. This paper concludes with remarks on conceptualizing the second-line funeral as a sophisticated trauma-informed approach to grief and a note for future research. [ABSTRACT FROM AUTHOR]

This article comprehensively reviews motor impairments in children with autism spectrum disorder (ASD) to: (1) determine the prevalence of motor problems in children with ASD; (2) understand the nature of motor difficulties in ASD and whether they are consistent with developmental coordination disorder (DCD); and (3) determine if the term DCD was used as a co-occurring diagnosis in children with ASD after publication of the DSM-5 in 2013. The following databases were systematically searched: MEDLINE, EMBASE, CINAHL, and PsycINFO from 2010 to December 2021. Articles were included if they: (1) were peer-reviewed and published in a scientific journal; (2) included children with ASD who were between 5 and 12 years; (3) used motor or function measures to assess motor abilities in children with ASD. Studies that included children with intellectual disabilities were excluded. Two independent reviewers reviewed titles, abstracts, and full-text articles for inclusion. Twenty-seven studies met the inclusion criteria and were assessed for quality by two independent reviewers using the Appraisal tool for Cross-Sectional Studies. The majority of articles (92.5%) indicated that 50–88% of children with ASD had significant motor impairments on standardized motor assessments and/or functional questionnaires. The nature of motor and function problems in ASD were consistent with DCD; however, only three out of 20 papers (15%) that were published from 2014 described the motor problems as DCD. One study reported that 15.1% of children with ASD with motor impairments had a co-occurring diagnosis of DCD, suggesting that DCD is under-recognized in this clinical population. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to describe and compare the proportion of patients classified as an emergency department (ED) mental health presentation under different definitions, including the Australian Institute of Health and Welfare (AIHW) definition. Methods: This retrospective cohort study enrolled all patients that presented to the EDs of a multi-centre Victorian health service between 1 January 2020 and 30 June 2023. Varying definitions of a mental health presentation were applied to each ED attendance, applying the current AIHW definition (using selected diagnosis codes), broader diagnosis-based coding, the presenting complaint recorded at triage and whether the patient was seen by or referred to the emergency psychiatric service (EPS). The proportion of all ED presentations meeting each definition and any overlap between definitions were calculated. The agreement between each definition and the AIHW definition was evaluated using Kappa's coefficient. Results: There were 813,078 presentations to ED of which 34,248 (4.2%) met the AIHW definition for a mental health presentation. Throughout the study, 45,376 (5.6%) patients were seen and/or referred to EPS, and 36,160 (4.4%) patients were allocated a mental health presenting complaint by triage staff. There was moderate interrater agreement between these definitions, with a kappa statistic (95% confidence interval) between the AIHW definition and a mental health presenting complaint recorded at triage of 0.58 (0.58–0.59) and between the AIHW definition and review by EPS of 0.58 (0.57–0.58). Conclusions: The AIHW definition is a conservative measure of ED mental health presentations and may underestimate emergency psychiatry workload in Australian EDs. What is known about this topic? It is unknown whether the Australian Institute of Health and Welfare (AIHW) definition of an emergency department (ED) mental health presentation definition accurately reflects ED mental health workload. What does this paper add? The AIHW definition appears to underestimate the clinical workload related to ED mental health presentations. There is moderate agreement between the AIHW definition and mental health presentations classified according to complaint recorded at triage, or whether the patient is reviewed by psychiatry services. What are the implications for practitioners? Accurate measurement of emergency psychiatry workload should not rely on the AIHW definition alone. [ABSTRACT FROM AUTHOR]

In diagnostic systems (e.g., DSM-5, ICD-10), depression is defined categorically. However, the concept of subthreshold depression (SD) has gained increasing interest in recent years. The purpose of the present paper was to review, based on a scoping review, the relevant papers in this field published between October 2011 and September 2020. Of the 1,160 papers identified, 64 records could be included in further analysis. The scoping review was conducted using both electronic and manual methods. The main result of the analysis is that the operationalisation criteria used are highly heterogeneous, which also leads to very heterogenous epidemiological data. Clear conclusions are not possible scrutinising the reported results. Most definitions seem to be arbitrary, with considerable overlap (e.g., between SD and minor depression). The review also revealed that the impact of SD on quality of life and related parameters appear to be in the range of the respective impact of major depression (MD) and therapeutic approaches might be helpful for SD and also for the prevention of conversion from SD to MD. Keeping the presented difficulties in mind, a proposal for the definition of SD is made in the present paper in order to facilitate the discussion leading to more homogeneous criteria. [ABSTRACT FROM AUTHOR]

Identifying major depression in children and adolescents is more challenging than in adults. Questionnaires are often used for screening or guiding clinical assessment. Several instruments of different lengths are used as equivalent measures in diagnostic decisions. In this paper, we explore to what extent 18 commonly used depression scales for children and adolescents explore depression clinical symptoms as established by standard DSM-5 diagnosis criteria. We analyzed scale content adequacy by examining the overlap between scale contents and consensus clinical symptoms, the diagnostic time frame for active symptom assessment, and readability for the target age group. The 18 scales encompassed 52 distinct symptoms. These scales included just 50% of clinical symptoms required for diagnosis. The content overlap was low; on average, 29% of symptoms coincide across scales. Half of the scales did not use the standard period for active symptom appraisal, and some did not include a period for assessment. The reading levels on six scales were inappropriate for the scale's target population age group. The substantial heterogeneity in defining the depressive syndrome, the low overlap among scales, different periods of a positive diagnosis, and mismatch of reading competence for detecting may lead to heterogeneity in clinical diagnoses when using different scales. Improving the content of self-report in terms of homogeneity of diagnostic criteria would lead to better diagnostic decisions and patient management. [ABSTRACT FROM AUTHOR]

The increasing prevalence of mental disorders among youth worldwide is one of society's most pressing issues. The proposed methodology introduces an artificial intelligence-based approach for comprehending and analyzing the prevalence of neurological disorders. This work draws upon the analysis of the Cities Health Initiative dataset. It employs advanced machine learning and deep learning techniques, integrated with data science, statistics, optimization, and mathematical modeling, to correlate various lifestyle and environmental factors with the incidence of these mental disorders. In this work, a variety of machine learning and deep learning models with hyper-parameter tuning are utilized to forecast trends in the occurrence of mental disorders about lifestyle choices such as smoking and alcohol consumption, as well as environmental factors like air and noise pollution. Among these models, the convolutional neural network (CNN) architecture, termed as DNN1 in this paper, accurately predicts mental health occurrences relative to the population mean with a maximum accuracy of 99.79%. Among the machine learning models, the XGBoost technique yields an accuracy of 95.30%, with an area under the ROC curve of 0.9985, indicating robust training. The research also involves extracting feature importance scores for the XGBoost classifier, with Stroop test performance results attaining the highest importance score of 0.135. Attributes related to addiction, namely smoking and alcohol consumption, hold importance scores of 0.0273 and 0.0212, respectively. Statistical tests on the training models reveal that XGBoost performs best on the mean squared error and R-squared tests, achieving scores of 0.013356 and 0.946481, respectively. These statistical evaluations bolster the models' credibility and affirm the best-fit models' accuracy. The proposed research in the domains of mental health, addiction, and pollution stands to aid healthcare professionals in diagnosing and treating neurological disorders in both youth and adults promptly through the use of predictive models. Furthermore, it aims to provide valuable insights for policymakers in formulating new regulations on pollution and addiction. [ABSTRACT FROM AUTHOR]

Purpose: Research objects, such as datasets and classification standards, are difficult to be incorporated into a document-centric framework of citations, which relies on unique citable works. The Diagnostic and Statistical Manual for Mental Disorder (DSM)—a dominant classification scheme used for mental disorder diagnosis—however provides a unique lens on examining citations to a research object, given that it straddles the boundaries as a single research object with changing manifestations. Design/methodology/approach: Using over 180,000 citations received by the DSM, this paper analyzes how the citation history of DSM is represented by its various versions, and how it is cited in different knowledge domains as an important boundary object. Findings: It shows that all recent DSM versions exhibit a similar citation cascading pattern, which is characterized by a strong replacement effect between two successive versions. Moreover, the shift of the disciplinary contexts of DSM citations can be largely explained by different DSM versions as distinct epistemic objects. Practical implications: Based on these results, the authors argue that all DSM versions should be treated as a series of connected but distinct citable objects. The work closes with a discussion of the ways in which the existing scholarly infrastructure can be reconfigured to acknowledge and trace a broader array of research objects. Originality/value: This paper connects quantitative methods and an important sociological concept, i.e. boundary object, to offer deeper insights into the scholarly communication system. Moreover, this work also evaluates how versioning, as a significant yet overlooked attribute of information resources, influenced the citation patterns of citable objects, which will contribute to more material-oriented scientific infrastructures. [ABSTRACT FROM AUTHOR]

Aim: This study examined the relationships between patient‐centred care, diabetes self‐management and selected health outcomes for Omani patients with type‐2 diabetes. Methods: Cross‐sectional surveys were administered for 237 patients with type‐2 diabetes. Hierarchical regression analyses examined the relationships between the selected study variables. Results: The sample was middle‐aged (M = 48, SD = 11.0 years) with nearly a decade (M = 9.68, SD = 6.11) since being diagnosed with type‐2 diabetes, with higher than ideal glycaemic control measures (M = 8.8%, SD = 2.4%). In bivariate analyses, patient‐centred care was positively associated with diabetes self‐management but not with glycaemic control or quality of life. However, after controlling demographic and clinical characteristics, patient‐centred care was positively associated with both physical and mental aspects of health‐related quality of life. In the final regression model, controlling for demographic and clinical characteristics and patient‐centred care, diabetes self‐management significantly predicted both glycaemic control and both physical and mental aspects of health‐related quality of life. Conclusions: The findings support that providing individualized care may contribute to the self‐management of chronic conditions. For achieving optimal outcomes, it may be necessary for providers to effectively assess patients' unique challenges and motivations. Increasing diabetes self‐management may improve quality of life and reduce diabetes complications. Summary statement: What is already known about this topic? Patient‐centred care (PCC) enhances diabetes self‐management (DSM).Little is known about the primary care nurse's role in promoting DSM within a PCC model. What this paper adds? PCC contributes to DSM and improves patient health outcomes.Expecting patients with diabetes to comply with provided instructions may not be enough to change self‐management behaviours. The implications of this paper Administrators responsible for employing a PCC approach in their clinics may need to establish clear goals and expectations for each team member to achieve overall success and ensue that nurses and physicians have targeted training on specific strategies to encourage patient‐provider partnerships that support the adoption of effective DSM behaviours.As the patient‐centred model of care is implemented in Oman, medical and nursing education programmes may highlight in their curricula its potential impact upon self‐management behaviours.Observational studies and longitudinal research to monitor Oman's progress towards the transition to a PCC model of care may uncover modifiable barriers and facilitators that primary care providers encounter. [ABSTRACT FROM AUTHOR]

The author replies to a critique of his paper about false memories in an effort to clarify issues on which there is disagreement. A key point made is that the validity of dissociation as a phenomenon, that is, as reported symptoms, does not depend on theories about the mechanisms of dissociation. There are no proven mechanisms for any mental health symptoms or DSM-5 diagnoses including anxiety, depression, psychosis and substance use disorders. [ABSTRACT FROM AUTHOR]

In this paper, I examine Reinforcement Learning (RL) modelling practice in psychiatry, in the context of alcohol use disorders. I argue that the epistemic roles RL currently plays in the development of psychiatric classification and search for explanations of clinically relevant phenomena are best appreciated in terms of Chang's (2004) account of epistemic iteration, and by distinguishing mechanistic and aetiological modes of computational explanation. [ABSTRACT FROM AUTHOR]

Accessible Summary: What is known on the subject?: Physical restraint is widely used in the elderly at home, and cognitive impairment is an important risk factor. Family caregivers of people with dementia are the main decision makers and implementers of physical restraint at home.Most people with dementia in China receive home care, and family caregivers face enormous care and moral pressures influenced by Confucian culture.Current research on physical restraints focuses on quantitative analysis of its prevalence and reasons within the institutions. There is little research on how family caregivers perceive physical restraints in home care context, especially under Chinese culture. What the paper adds to existing knowledge?: Many family caregivers face approach – avoidance conflict and moral dilemmas when making decisions to restrain, and they make difficult choices in these dilemmas.In China, family caregivers are influenced by many unique factors, including traditional Confucian culture, family affection, and rural home environment.Inadequate laws and policies provide the ground for abuse of physical restraints, and family caregivers rarely consider legal and policy restrictions when using physical restraints. What are the implications for practice?: With limited medical resources, nurse‐led dementia management is the hope to reduce physical restraints in home.Mental health nurses need to assess for the appropriateness of physical restraints associated with psychiatric symptoms in people with dementia.At both organizational and community levels, improving effective communication and relationships between professionals and family caregivers are important to address. Improvements in this context require education and time for staff to develop skills and experience which is necessary to provide family caregivers with ongoing information and psychological support within their communities.Considering Confucian culture will be of value for mental health nurses working in other countries where there are Chinese communities to better understand perceptions of family caregivers. Introduction: The use of physical restraints is a common practice in home care. Family caregivers face care‐related and moral pressures due to the influence of Confucian culture in China. The use of physical restraints in the Chinese cultural environment may differ from the use of such restraints in other cultures. Scientific Rationale: Current research on physical restraints focuses on quantitative analysis of its prevalence and reasons within the institutions. However, there is little research on how family caregivers perceive physical restraints in home care context, especially under Chinese culture. Aim: To explore the perceptions of family caregivers on physical restraints in people diagnosed with dementia receiving home care. Method: A descriptive, qualitative study of Chinese family caregivers of people diagnosed with dementia in home care. Framework method analysis was adopted using the multilevel socio‐ecological model. Results: Beliefs about benefit lead to a dilemma for family caregivers. Cherishing family's affection encourages caregivers to reduce physical restraints, but lack of help from family members, professionals and the community forces them to restrain their loved ones. Discussion: Future research should explore the complex issue of culturally specific physical restraints decisions. Implications for Practice: Mental health nurses must receive education regarding the negative outcomes of the use of physical restraints for family members of people diagnosed with dementia. A more liberal approach to mental health and relevant legislation, which is an emerging global phenomenon that is currently in an early phase of development in China, grants human rights to people diagnosed with dementia. Effective communication and relationships between professionals and family caregivers can contribute to the establishment of a dementia‐friendly community in China. [ABSTRACT FROM AUTHOR]

Aim: To investigate the mental well‐being of early career nurses working in the United Kingdom during the COVID‐19 pandemic, with a particular emphasis on symptoms related to post‐traumatic stress disorder. Design: A longitudinal survey study. Methods: Data were acquired at three timepoints during the COVID‐19 pandemic (between May 2020 and March 2021) to determine whether symptoms of post‐traumatic stress disorder persisted over time. Quantitative measures of well‐being were supplemented with survey data on the nurses' experiences of working during the pandemic. Results: Twenty‐seven per cent of participants suffered from persistent symptoms of post‐traumatic stress while working as nurses during the pandemic. The nurses' baseline resilience, as well as their perception of the quality of their work environment, were significant negative predictors of symptoms of post‐traumatic stress. Participants identified a range of strategies that would have helped them during the crisis, including visible, consistent and empathetic leadership, adequate training and a supportive work environment. Conclusion: The context of the pandemic has highlighted the vulnerability of the psychological well‐being of early career nurses in the workforce. Immediate implementation of some of the more simple interventions suggested in this paper would provide early career nurses with rapid support. More complex support mechanisms should be given immediate consideration, with a view to implementation in the longer term. Implications for the Profession: This study contributes new knowledge about the psychological well‐being of early career nurses working during the pandemic and suggests support mechanisms that will be crucial for the retention of these nurses in the profession. A measurement of resilience may be useful for determining the appropriate level of support to provide to early career nurses. Impact: Early career nurses are vulnerable to attrition from the profession. This could be exacerbated if the psychological well‐being of these nurses is not being supported.Around 25% of early career nurses suffered from persistent symptoms of post‐traumatic stress disorder while working as nurses during the height of the pandemic, which is a novel finding compared to other longitudinal studies.Understanding the psychological well‐being of early career nurses working during a crisis period (such as a pandemic) equips nurse managers with appropriate strategies to improve nurses' emotional health and to enhance their retention within the workforce.The current findings may be of interest to clinical practitioners who have responsibility for the retention of nursing staff. No patient or public contribution. One of the authors is a statistician. [ABSTRACT FROM AUTHOR]

An intense level of academic pressure causes students to experience stress, and if this stress is not addressed, it can cause adverse mental and physical effects. Since the pandemic situation, students have received more assignments and other tasks due to the shift of classes to an online mode. Students may not realize that they are stressed, but it may be evident from other factors, including sleep deprivation and changes in eating habits. In this context, this paper presents a novel ensemble learning approach that proposes an architecture for stress level classification. It analyzes certain factors such as the sleep hours, productive time periods, screen time, weekly assignments and their submission statuses, and the studying methodology that contribute to stress among the students by collecting a survey from the student community. The survey data are preprocessed to categorize stress levels into three categories: highly stressed, manageable stress, and no stress. For the analysis of the minority class, oversampling methodology is used to remove the imbalance in the dataset, and decision tree, random forest classifier, AdaBoost, gradient boost, and ensemble learning algorithms with various combinations are implemented. To assess the model's performance, different metrics were used, such as the confusion matrix, accuracy, precision, recall, and F1 score. The results showed that the efficient ensemble learning academic stress classifier gave an accuracy of 93.48% and an F1 score of 93.14%. Fivefold cross-validation was also performed, and an accuracy of 93.45% was achieved. The receiver operating characteristic curve (ROC) value gave an accuracy of 98% for the no-stress category, while providing a 91% true positive rate for manageable and high-stress classes. The proposed ensemble learning with fivefold cross-validation outperformed various state-of-the-art algorithms to predict the stress level accurately. By using these results, students can identify areas for improvement, thereby reducing their stress levels and altering their academic lifestyles, thereby making our stress prediction approach more effective. [ABSTRACT FROM AUTHOR]

The early prediction of mental illnesses reduces the severity of the disease. The symptoms like poor concentration, unstable energy of the body, pressure, and loss of interest cause depression. A large-scale group decision making (LSGDM) method is proposed in this paper along with the ensemble classifier model by combining convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM) for effective classification of depression, anxiety and stress (DAS) levels. The data is collected from the depression anxiety stress scale-42 (DASS-42) dataset for efficient classification and predictions of mental health problems. The min-max normalization is used to pre-process the data, and the feature selection is done for extracting informative features. The extracted features are provided as input to the ensemble classifier. The proposed LSGDM model maximizes the classification accuracy with the help of grid search CV hyperparameter tuning, and results in an accuracy of 98.88%, precision of 98.21%, recall of 99.62%, F1-Score of 98.90%, and MCC of 99.41%. The proposed LSGDM method gives superior results when compared to the existing machine learning (ML) based ensemble model, a principal component analysis along with modified fast correlation based filtering (PCA-mFCBF), and LSTM based RNN (LSTM-RNN). [ABSTRACT FROM AUTHOR]

Psychiatric clinical diagnostic formulation has evolved over time. The changes alter our understanding and our ability to provide a public health perspective on the epidemiology of mental disorders in large populations. Epidemiology is an important perspective and set of tools to assess prevalence, treated prevalence, untreated prevalence, individual risks for mental disorders, and possible links to the etiology of disorders by following the trails of environmental exposures, biological measures, interpersonal dynamics, and genetic risk factors. As communities develop health-care services to treat individuals with mental disorders, knowledge about their prevalence and treatment requirements is also important. Since severe mental disorders may require institutional care, the diagnostic criteria used may either protect an individual’s liability for dangerous behavior (i.e. the insanity defense) or be used to control political and social dissidents. The criteria may also be used to determine evidence-based treatment options and eligibility for disability benefits. In this paper, using my vantage points as a physician scientist and public health officer, with leadership positions in national federal and professional mental health organizations, I address the developments in these areas over the past 50 years that have influenced institutional positions in the U.S. National Institute of Mental Health, the World Health Organization, the American Psychiatric Association, and the Center for the Study of Traumatic Stress. These perspectives may aid the next generation of investigators to advance the epidemiological and mental disorder classification scientific fields. [ABSTRACT FROM AUTHOR]

The present paper highlights how alcohol use disorder (AUD) conceptualizations and resulting diagnostic criteria have evolved over time in correspondence with interconnected sociopolitical influences in the United States. We highlight four illustrative examples of how DSM-defined alcoholism, abuse/dependence, and AUD have been influenced by sociopolitical factors. In doing so, we emphasize the importance of recognizing and understanding such sociopolitical factors in the application of AUD diagnoses. Last, we offer a roadmap to direct the process of future efforts toward the improved diagnosis of AUD, with an emphasis on pursuing falsifiability, acknowledging researchers' assumptions about human behavior, and collaborating across subfields. Such efforts that center the numerous mechanisms and functions of behavior, rather than signs or symptoms, have the potential to minimize sociopolitical influences in the development of diagnostic criteria and maximize the treatment utility of diagnoses. [ABSTRACT FROM AUTHOR]

A serious issue in today's society is Depression, which can have a devastating impact on a person's ability to cope in daily life. Numerous studies have examined the use of data generated directly from users using social media to diagnose and detect Depression as a mental illness. Therefore, this paper investigates the language used in individuals' personal expressions to identify depressive symptoms via social media. Graph Attention Networks (GATs) are used in this study as a solution to the problems associated with text classification of depression. These GATs can be constructed using masked self-attention layers. Rather than requiring expensive matrix operations such as similarity or knowledge of network architecture, this study implicitly assigns weights to each node in a neighbourhood. This is possible because nodes and words can carry properties and sentiments of their neighbours. Another aspect of the study that contributed to the expansion of the emotion lexicon was the use of hypernyms. As a result, our method performs better when applied to data from the Reddit subreddit Depression. Our experiments show that the emotion lexicon constructed by using the Graph Attention Network ROC achieves 0.91 while remaining simple and interpretable. [ABSTRACT FROM AUTHOR]

Research on Black/African American males' depressive experiences and expressions is still developing. While research has shown that men are less likely to be diagnosed with depression when compared to women, the experiences of Black/African Americans are often ignored at a far greater level. This study aimed to highlight how Black/African American men understood, discussed, and dealt with their depression. Interviews of N = 4 Black/African American men were analyzed from a larger qualitative study that examined depression among Blacks/African Americans. Qualitative content analysis techniques, which included open coding, were used to identify themes within the interviews that identified similarities across participant's experiences. The themes highlighted within this report included: (a) depressive episodes as responses to traumatic life events; (b) family and paternal significance for Black/African American men; (c) substance use and sleeping as coping/treatment; and (d) stigma as the greatest impediment to help-seeking and service use. Blacks/African Americans experienced depression within the classical contextualization of the DSM-V. However, they quickly learned to adapt to their experiences, push through the pain, and identify their experiences as normal parts of everyday living. The paper ends with some recommended practice implications for service providers who are interested in working with Black/African American men. [ABSTRACT FROM AUTHOR]

With the impact of the COVID-19 pandemic, the number of patients suffering from depression is rising around the world. It is important to diagnose depression early so that it may be treated as soon as possible. The self-response questionnaire, which has been used to diagnose depression in hospitals, is impractical since it requires active patient engagement. Therefore, it is vital to have a system that predicts depression automatically and recommends treatment. In this paper, we propose a smartphone-based depression prediction system. In addition, we propose depressive features based on multimodal sensor data for predicting depressive mood. The multimodal depressive features were designed based on depression symptoms defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed system comprises a "Mental Health Protector" application that collects data from smartphones and a big data-based cloud platform that processes large amounts of data. We recruited 106 mental patients and collected smartphone sensor data and self-reported questionnaires from their smartphones using the proposed system. Finally, we evaluated the performance of the proposed system's prediction of depression. As the test dataset, 27 out of 106 participants were selected randomly. The proposed system showed 76.92% on an f1-score for 16 patients with depression disease, and in particular, 15 patients, 93.75%, were successfully predicted. Unlike previous studies, the proposed method has high adaptability in that it uses only smartphones and has a distinction of evaluating prediction accuracy based on the diagnosis. [ABSTRACT FROM AUTHOR]

Assen Jablensky, a renowned psychiatrist, made significant contributions to the understanding of schizophrenia and other psychotic disorders. His work focused on the etiology, epidemiology, and manifestations of these disorders in different cultures. Jablensky held various roles in multiple countries, including Bulgaria, Switzerland, the UK, and the USA, before settling in Australia. He conducted research, served as a clinician, teacher, mentor, and administrator, and published numerous books and scientific papers. Jablensky's work was internationally recognized, and he received several awards and honors for his contributions to the field of psychiatry. [Extracted from the article]

This paper examines whether U.S./Mexico border residence in California is related to the prevalence of DSM-5 alcohol use disorder (AUD) among Whites and Hispanics. Household survey data were obtained from 1,209 adults (59.7% female) 18 to 39 years of age resident in four counties in California: Imperial on the U.S./Mexico border; and Kern, Tulare, and Madera in California's Central Valley. Households were selected using a list assisted sample, with data collected on the phone or online. Results show that AUD rates were not different between border and non-border location and between Whites and Hispanics. AUD was negatively associated with higher income ($20,000 to $60,000: AOR=.38; 95%CI=.17–.86; p<.01—more than $60,000: AOR=.27; 95%CI:.09–.81; p<.01) and poor risk perception (AOR=.86; 95%CI=.78–.94; p<.01). AUD was positively associated with continued volume of drinking (AOR = 1.05; 95%CI = 1.01–1.09; p<.01), drinking in Mexico (AOR = 4.28; 95%CI = 1.61–11.36; p<.01), marijuana use (AOR = 4.11; 95%CI = 1.73–9.77; p<.01), and impulsivity (AOR = 1.55; 95%CI = 1.23–1.94). Efforts to prevent AUD in the population in California, and especially among those who live close to the border with Mexico, should take into consideration factors such as impulsivity, marijuana use, border crossing to drink in Mexico, all of which increased risk of AUD. [ABSTRACT FROM AUTHOR]

According to the literature, comorbidity rates observed on emotional disorders are linked to how the main diagnostic classification systems have traditionally defined these disorders. This paper aims to analyze the structure of symptoms evaluated with the Inventory of Depression and Anxiety Symptoms-II (IDAS-II) with network analysis. A mixed sample (n = 2021) of 1692 community adults and 329 patients was used. 14.79% (n = 299) of the sample met the diagnostic criteria for at least one DSM-5 mental disorder and 5.29% (n = 107) had diagnostic comorbidity. The sample was randomly divided into two sub-samples: estimation sample (n = 1010) and replication sample (n = 1011). The detection of community structures was carried out on estimation sample using the walktrap algorithm. Four local inference measures were estimated: Strength, one-step Expected Influence, two-step Expected Influence, and node predictability. Exploratory graphic analysis of modularity yielded an optimal solution of two communities on estimation sample: first linked to symptoms of depression and anxiety and second grouping symptoms of bipolar disorder and obsessive – compulsive disorder. Mania, Panic, Claustrophobia, and Low Well-Being Bridge emerged as bridge symptoms, connecting the two substructures. Networks estimated on replication subsamples did not differ significantly in structure. Dysphoria, Traumatic Intrusions and Checking and Ordering were the symptoms with greatest number of connections with rest of the network. Results sheds light on specific links between emotional disorder symptoms and provides useful information for the development of transdiagnostic interventions by identifying the influential symptoms within the internalizing spectrum. [ABSTRACT FROM AUTHOR]

In today's world, stress is a major factor for various diseases in modern societies which affects the day-to-day activities of human beings. The measurement of stress is a contributing factor for governments and societies that impacts the quality of daily lives. The strategy of stress monitoring systems requires an accurate stress classification technique which is identified via the reactions of the body to regulate itself to changes within the environment through mental and emotional responses. Therefore, this research proposed a novel deep learning approach for the stress classification system. In this paper, we presented an Enhanced Long Short-Term Memory(E-LSTM) based on the feature attention mechanism that focuses on determining and categorizing the stress polarity using sequential modeling and word-feature seizing. The proposed approach integrates pre-feature attention in E-LSTM to identify the complicated relationship and extract the keywords through an attention layer for stress classification. This research has been evaluated using a selected dataset accessed from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze health-related stress data. Statistical performance of the developed approach was analyzed based on the nine features of stress detection, and we compared the effectiveness of the developed approach with other different stress classification approaches. The experimental results shown that the developed approach obtained accuracy, precision, recall and a F1-score of 75.54%, 74.26%, 72.99% and 74.58%, respectively. The feature attention mechanism-based E-LSTM approach demonstrated superior performance in stress detection classification when compared to other classification methods including naïve Bayesian, SVM, deep belief network, and standard LSTM. The results of this study demonstrated the efficiency of the proposed approach in accurately classifying stress detection, particularly in stress monitoring systems where it is expected to be effective for stress prediction. [ABSTRACT FROM AUTHOR]

Western societies over the last few decades have seen an increased interest in questions of group belonging and group identities, including ethno‐national groups. According to essentialising or constructivist paradigms, belonging to a national group is commonly conceptualised in the range of objective versus subjective criteria, where objective entails ascription and subjective, self‐identification. This paper suggests disentangling the paired dimensions—objective and other‐classification versus subjective and self‐classification—by analysing the late Habsburg Empire. I argue that the introduction of national registers in the new provincial constitutions and electoral laws of Moravia, Bukovina and Galicia accelerated an objective understanding of nationality and increasingly favoured other‐classification over self‐classification in cases where national belonging had become an administrative category. Yet, to do justice to the individual's subjective feelings, authorities were supposed to investigate objectively a citizen's subjective identification—a procedure that can be termed "objectivisation." Such objectivising procedures thus reconciled an individual's subjective identification with an increasingly objective understanding of nationality by developing guidelines on how classification by others should proceed. [ABSTRACT FROM AUTHOR]

The use of alcohol and psychoactive substances manifests itself in the rapid destruction of behavioral mechanisms. Dependence on these substances suppresses a person's behavioral and personal traits. The purpose of the academic paper is to determine the specifics of mental and behavioral disorders that may arise and develop as a result of a person's use of alcohol and psychoactive substances, to establish tendencies in studying this issue in scientific circles, and to clarify certain practical aspects of this problem. In the course of the research, an analytical and bibliographic method was used to study the scientific literature on mental and behavioral disorders that occur as a result of using alcohol and psychoactive substances. Induction, deduction, analysis, synthesis of information, systemstructural, comparative, logical-linguistic methods, abstraction, and idealization were applied to study and process data. At the same time, a questionnaire in online mode was conducted by the research authors for the practical clarification of particular aspects of mental and behavioral disorders due to using psychoactive substances and alcohol. Based on the research results, the principal and most important theoretical aspects of problems related to mental and behavioral disorders that may arise and develop as a result of a person's use of alcohol and psychoactive substances are identified. Along with this, a standpoint of scientists-teachers of medical specialties of higher educational institutions and narcologists was studied regarding the key practical aspects of the issue outlined. [ABSTRACT FROM AUTHOR]

A developmental understanding of the expression of posttraumatic stress (PTS) symptoms following trauma in childhood requires identifying continuity and change in not just overall symptoms but in the individual symptoms as well. Such models of change also require understanding multiple dimensions of time. That is, longitudinal change—the passage of time—may have different effects on symptom expression depending on when in time an individual entered the study – such as what age they were when first assessed. This paper addresses these ideas with an overview of the developmental differences in the assessment of PTS from the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders, (DSM-5), longitudinal research on posttraumatic stress disorder (PTSD) symptoms, and providing synthesis within a symptom network perspective. We then provide an illustration of how individual PTSD symptoms change over time at different ages (elementary versus middle school) with a secondary analysis of data from a previously reported study (n = 191 youth, aged 8–15, assessed at two time points six months apart). The reanalysis of the data suggests both continuity and change in symptoms over time (i.e., some symptoms were more stable than other symptoms) with differences in symptom rates and their longitudinal change as a function of age (i.e., some symptoms more common or more stable in younger versus older or older versus younger). We close with avenues for future research aimed at better understanding symptom cascades over time and at different ages and potential implications for future iterations of assessment/classification systems. [ABSTRACT FROM AUTHOR]

The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing efforts, neurological observations alongside clinical evaluations are considered to be potential solutions to address diagnostic problems. The Bipolar‐Schizophrenia Network on Intermediate Phenotypes (B‐SNIP) has published multiple papers attempting to reclassify psychotic illnesses based on biological rather than symptomatic measures. However, the effort to investigate the relationship between this new categorization approach and other neuroimaging techniques, including resting‐state fMRI data, is still limited. This study focused on investigating the relationship between different psychotic disorders categorization methods and resting‐state fMRI‐based measures called dynamic functional network connectivity (dFNC) using state‐of‐the‐art artificial intelligence (AI) approaches. We applied our method to 613 subjects, including individuals with psychosis and healthy controls, which were classified using both the Diagnostic and Statistical Manual of Mental Disorders (DSM‐IV) and the B‐SNIP biomarker‐based (Biotype) approach. Statistical group differences and cross‐validated classifiers were performed within each framework to assess how different categories. Results highlight interesting differences in occupancy in both DSM‐IV and Biotype categorizations compared to healthy individuals, which are distributed across specific transient connectivity states. Biotypes tended to show less distinctiveness in occupancy level and included fewer cellwise differences. Classification accuracy obtained by DSM‐IV and Biotype categories were both well above chance. Results provided new insights and highlighted the benefits of both DSM‐IV and biology‐based categories while also emphasizing the importance of future work in this direction, including employing further data types. [ABSTRACT FROM AUTHOR]

The treatment of schizoid personality disorder (SZPD) is a poorly studied area of psychotherapy. Although the construct of a schizoid personality has been defined for the last century, no clinical guidelines or evidence-based practices have been developed specifically for the treatment of SZPD. The cases series presented in this paper aim to explore the effect of an integrative psychotherapy for patients diagnosed with schizoid personality disorder. Two therapies that showed good evidence in supporting people with recurrent symptoms and mechanisms related to schizoid personality were integrated: Metacognitive Reflection and Insight Therapy and Compassion Focused Therapy. The former has been shown to be effective in treating schizophrenia spectrum disorders and psychosis and their associated negative symptoms, whereas the latter has been found to promote self-soothing and social safeness against a wide range of symptoms. This proposed integrative form of psychotherapy was tested by recruiting two patients diagnosed with schizoid personality disorder. The intervention showed reliable changes in symptomatology and remission from diagnosis. No adverse events were reported. Future research should confirm these findings regarding the feasibility of the proposed intervention. [ABSTRACT FROM AUTHOR]

This paper explores a collaborative generative approach to psychological assessment employed in Puerto Rico, with children, adolescents, and adults. It begins with a critical reflection on how the dominant discourse of mental health, founded in the suppositions and practices of Euro‐American‐centred psychological knowledge, has been disseminated to the globalise south under the claim that its assertions are unaffected by social, ideological, or historical forces. It discusses how its claims of expert scientific knowledge have contributed, through classificatory instruments such as the DSM and the ICD, to the production and re‐production of deficit narratives in our day‐to‐day life. It also examines how these practices have been applied in the use of assessment instruments in Puerto Rico. It then describes, how, through a collaborative approach and narrative theory, the established colonising practices and narratives of traditional forms of psychological assessment can be questioned, deconstructed, and transformed. This approach promotes the co‐creation of dialogic and generative spaces that allow for the emergence of multiple stories and performances that give meaning to a person's identity and relational being. A brief clinical case exposition is used to illustrate how this collaborative, dialogic, and culturally sensitive approach to psychological assessment can help to undermine and disrupt deficit‐based narratives and provide families with new generative possibilities for re‐storying and re‐performing their lives and particularly, the lives of their children. [ABSTRACT FROM AUTHOR]

Background: Stroke remains a major leading cause of morbidity and death globally. For ischemic stroke, the most frequent type of stroke, there are numerous risk models and risk assessments offered. Further research into potential risk factors or triggers is being sought to improve stroke risk models. Schizophrenia, bipolar disorder, and alcohol use disorder are all common causes of serious mental illnesses in the general population. Due to the tangled relationship between stroke and many chronic illnesses, lifestyle factors, and diet that may be present in a patient with a mental disease, the relationship between mental diseases and stroke requires further validation. Consequently, the purpose of this study is to assess the potential influence of bipolar disorder, schizophrenia, and alcohol use disorder on stroke patients as compared to non-stroke participants, after controlling for demographic, physical, and medical conditions. We aimed, as a secondary objective, to evaluate the impact of these pre-existing disorders on stroke severity levels. Methods: This research is a case-control survey study involving 113 Lebanese patients with a clinical diagnosis of ischemic stroke and 451 gender-matched volunteers without clinical signs of stroke as controls recruited from several hospitals in Lebanon (April 2020–April 2021). Based on the participant's consent, data was collected by filling out an anonymous paper-based questionnaire. Results: All of the odds ratios (ORs) generated by our regression model were greater than 1, indicating that the factors studied were associated with an increased risk of ischemic stroke. As such having schizophrenia (adjusted OR [aOR]: 6.162, 95% confidence interval [CI]: 1.136–33.423), bipolar disorder (aOR: 4.653, 95% CI: 1.214–17.834), alcohol use disorder (aOR: 3.918, 95% CI: 1.584–9.689), atrial fibrillation (aOR: 2.415, 95% CI: 1.235–4.721), diabetes (aOR: 1.865, 95% CI: 1.117–3.115), heart diseases (aOR: 9.890, 95% CI: 5.099–19.184), and asthma-COPD (aOR: 1.971, 95% CI: 1.190–3.263) were all involved with a high risk of developing an ischemic stroke. Moreover, obesity (aOR: 1.732, 95% CI: 1.049–2.861) and vigorous physical activity (aOR: 4.614, 95% CI: 2.669–7.978) were also linked to an increased risk of stroke. Moreover, our multinomial regression model revealed that the odds of moderate to severe/severe stroke were significantly higher in people with pre-stroke alcohol use disorder (aOR: 1.719, 95% CI: 1.385–2.133), bipolar disorder (aOR: 1.656, 95% CI: 1.281–2.141), and schizophrenia (aOR: 6.884, 95% CI: 3.294–11.492) compared to people who had never had a stroke. Conclusion: The findings in our study suggest that individuals with schizophrenia, bipolar disorder, and alcohol use disorder may be at a higher risk for ischemic stroke and exhibit more severe symptoms. We believe that the first step toward creating beneficial preventative and treatment interventions is determining individuals with schizophrenia, bipolar disorder, or alcohol use disorder, assessing their risk of ischemic stroke, developing more integrated treatments, and closely monitoring the long-term outcome in the event of an ischemic stroke. [ABSTRACT FROM AUTHOR]

Functional neurological disorder (FND) is a disorder of the autonomic motor or sensory system with well-defined symptoms, which covers functional movement disorder, psychogenic non-epileptic seizures, functional sensory disorder, functional cognitive disorder, persistent postural perceptual dizziness and so on. Its complex and diverse characteristics make the diagnosis and treatment of the disease and research difficult, so FND has long been a "gray area", but its high incidence, disability rate and high consumption of medical resources remind us that FND cannot be ignored. This paper reviews the current clinical classification of FND and the diagnostic criteria in order to improve the diagnosis and treatment by domestic colleagues. [ABSTRACT FROM AUTHOR]

Difficulties in recruiting newly bereaved families and following them over time present a major barrier in grief research following the death of a spouse/parent. We established FALCON—the first prospective nationwide cohort of families with children below age 18 years whose parent died in Denmark between April 2019 and July 2021. Data from parents and children were collected within 2 months of death with ongoing follow-up assessments up to 18 months post-death. A total of 992 families were invited. The final cohort consisted of 250 families (250 widowed parents, 134 adolescents, 120 children aged 6–12 years and 63 children aged 0–5 years). In this paper, we describe the rationale for the cohort's creation, the challenges of researching grief in families, the methods used and future plans to utilize this unique family-level dataset. [ABSTRACT FROM AUTHOR]

The use of philosophical phenomenology for conceptual debates in psychiatric nosology and psychopathology is beginning to be recognized. In this paper, we extend this trajectory to include Tourette syndrome (TS), focusing on so-called premonitory urges (PU) preceding Tourettic tics. We clarify some inconsistencies around typology in both phenomenological description and medical classification (i.e. in the Diagnostic and Statistical Manual of Mental Disorders , 5th edition, Text Revision, International Classification of Diseases , 10th edition [World Health Organization, 2004], and the scales that elicit PU). We show how a less typological approach might be usefully deployed in the field, that being the dimensional approach developed by Fernandez (2019a; 2019b). We test both the typological and dimensional approaches in relation to the experience and etiological role of PU in Tourette syndrome. Based on our synthesis of existing studies and new information garnered through phenomenological interviews (using an approach associated with "micro-phenomenology"), we argue that the role of PU in nosology challenges both the current "operational" criteria favored by the Diagnostic and Statistical Manual of Mental Disorders , 5th edition, Text Revision, that are focused on behaviorally observed symptoms, as well as essential and prototypical phenomenological descriptions about a given "type" of experience. [ABSTRACT FROM AUTHOR]

Type 1 diabetes is associated with increased prevalence of individual categories of mental disorders. We aimed to systematically synthesise the prevalence of all the different categories of mental disorders to estimate the overall burden of psychiatric morbidity in the type 1 diabetes population. The electronic database of OVID was searched, and retrieved papers were screened for eligibility by two independent reviewers. Data were extracted using a standardised data extraction form and the quality of included papers was assessed. Where possible, comparisons with control groups without type 1 diabetes were made. Prevalence data were synthesised into Diagnostic and Statistical Manual of Mental Disorders version 5 categories, a narrative data-synthesis, and a subsequent meta-analysis where possible was conducted for mental disorder categories. Thirty-eight articles were included. Depressive, anxiety, and feeding and eating disorders were the most examined mental disorders. Studies utilising diagnostic interviews reported higher prevalence of mental disorders than in studies utilising clinical registers, with an up to 24-fold difference respectively. In studies with a control group, the prevalence for nearly every mental disorder were increased for the type 1 diabetes samples. There appears to be a high prevalence of mental disorders and associated need among people with type 1 diabetes, although the quality of research needs to improve. This protocol was submitted for registration with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42020221530). [ABSTRACT FROM AUTHOR]

Catatonia is a complex neuropsychiatric syndrome, occurring in the context of different psychiatric and neurodevelopmental disorders, in neurological and medical disorders, and after substance abuse or withdrawal. The relationship between Autism Spectrum Disorder (ASD), Schizophrenia Spectrum Disorders (SSDs) and catatonia has been previously discussed, with the three disorders interpreted as different manifestations of the same underlying brain disorder (the "Iron Triangle"). We discuss in this paper the diagnostic, clinical and therapeutic implications of this complex relationship in an adolescent with ASD, who presented an acute psychotic onset with catatonia, associated with mixed mood symptoms. Second-generation antipsychotics were used to manage psychotic, behavioral and affective symptoms, with worsening of the catatonic symptoms. In this clinical condition, antipsychotics may be useful at the lowest dosages, with increases only in the acute phases, especially when benzodiazepines are ineffective. Mood stabilizers with higher GABAergic effects (such as Valproate and Gabapentin) and Lithium salts may be more useful and well tolerated, given the frequent association of depressive and manic symptoms with mixed features. [ABSTRACT FROM AUTHOR]

Oppression refers to systemic discrimination where the injustice targets or disproportionately impacts specific groups of people. The Trauma Symptoms of Discrimination Scale (TSDS) is a self-report measure designed to assess the traumatizing impact of discrimination broadly by measuring anxiety-related symptoms of trauma due to discriminatory experiences. This may include symptoms arising from racism, homophobia, sexism, poverty, or other forms of marginalization. Almost all studies of the TSDS have examined its use in marginalized ethnoracial groups, primarily African Americans. This paper will extend prior work to help us better understand racial trauma across groups by reporting and comparing TSDS mean scores across ethnoracial identities in a diverse national sample (n = 923). It also explores trauma with other marginalized identities and demographic dimensions, including gender, sexual minority/LGBQ status, education, and income. The relationship of TSDS scores to clinical psychopathologies are examined, including stress, depression, anxiety, and PTSD. We also examine the unique risks associated with intersectionality, and how having multiple marginalized identities may increase traumatization. Clinical implications and future directions are discussed. [ABSTRACT FROM AUTHOR]

The term racial trauma is used to describe the cumulative distressing and traumatizing effects of racism in all of its forms, and it closely resembles the construct of posttraumatic stress disorder (PTSD). This investigation aims to increase our understanding of racial trauma by comparing the characteristics of those with a clinically-relevant diagnosis of racial trauma to those without, based on the findings of a clinical semi-structured interview and symptom checklist for assessing racial trauma, the University of Connecticut Racial Ethnic Stress and Trauma Survey (UnRESTS), administered to a diverse group of adults (N = 97). This paper extends prior work on racial trauma by examining the correlations between racial trauma and validated self-report measures of discriminatory distress, controlling for racialization. We examine the correlation between a clinically-relevant diagnosis of racial trauma and racial/ethnic identity. We also compare racism-related PTSD symptoms in those with and without racial trauma to inform clinical assessment. Finally, we examine the factor structure of racial trauma symptoms using the 24 items from the UnRESTS PTSD symptom checklist and compare these to current DSM-5 models. The structure of racial trauma symptoms differed from the DSM-5 4-factor model, as do other PTSD models in the research literature. Clinical and research implications are discussed. [ABSTRACT FROM AUTHOR]

Most people will experience bereavement grief, but some suffer from persistent or prolonged (PoP) grief, grief that used to be identified as "complicated" before recent DSM-5 and ICD-11 definitional developments. In 2020, a scoping literature review was undertaken to identify and consolidate contemporary evidence from research articles published in 2018 or 2019 in paper-based and open access peer-review journals on: (a) the incidence or prevalence of PoP grief, and (b) who develops it or is diagnosed as suffering from it, and correspondingly what causative factors or predictors are associated with PoP grief. All of the 11 identified research articles reported an incidence or prevalence rate, with these varying greatly, but with a much higher rate now that a 6-month post-death measurement timeframe is used. The predictors or factors among people who were identified as having PoP grief also varied considerably, although sudden and unexpected deaths were often implicated. [ABSTRACT FROM AUTHOR]

This paper describes a process to define a comprehensive list of exemplars for seven core Diagnostic and Statistical Manual (DSM) diagnostic criteria for autism spectrum disorder (ASD), and report on interrater reliability in applying these exemplars to determine ASD case classification. Clinicians completed an iterative process to map specific exemplars from the CDC Autism and Developmental Disabilities Monitoring (ADDM) Network criteria for ASD surveillance, DSM-5 text, and diagnostic assessments to each of the core DSM-5 ASD criteria. Clinicians applied the diagnostic exemplars to child behavioral descriptions in existing evaluation records to establish initial reliability standards and then for blinded clinician review in one site (phase 1) and for two ADDM Network surveillance years (phase 2). Interrater reliability for each of the DSM-5 diagnostic categories and overall ASD classification was high (defined as very good.60–.79 to excellent ≥.80 Kappa values) across sex, race/ethnicity, and cognitive levels for both phases. Classification of DSM-5 ASD by mapping specific exemplars from evaluation records by a diverse group of clinician raters is feasible and reliable. This framework provides confidence in the consistency of prevalence classifications of ASD and may be further applied to improve consistency of ASD diagnoses in clinical settings. [ABSTRACT FROM AUTHOR]

Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require caution, and even the smallest error in diagnosis can have negative repercussions. Medical errors in brain tumor diagnosis are common and frequently result in higher patient mortality rates. Magnetic resonance imaging (MRI) is widely used for tumor evaluation and detection. However, MRI generates large amounts of data, making manual segmentation difficult and laborious work, limiting the use of accurate measurements in clinical practice. As a result, automated and dependable segmentation methods are required. Automatic segmentation and early detection of brain tumors are difficult tasks in computer vision due to their high spatial and structural variability. Therefore, early diagnosis or detection and treatment are critical. Various traditional Machine learning (ML) techniques have been used to detect various types of brain tumors. The main issue with these models is that the features were manually extracted. To address the aforementioned insightful issues, this paper presents a hybrid deep transfer learning (GN-AlexNet) model of BT tri-classification (pituitary, meningioma, and glioma). The proposed model combines GoogleNet architecture with the AlexNet model by removing the five layers of GoogleNet and adding ten layers of the AlexNet model, which extracts features and classifies them automatically. On the same CE-MRI dataset, the proposed model was compared to transfer learning techniques (VGG-16, AlexNet, SqeezNet, ResNet, and MobileNet-V2) and ML/DL. The proposed model outperformed the current methods in terms of accuracy and sensitivity (accuracy of 99.51% and sensitivity of 98.90%). [ABSTRACT FROM AUTHOR]

The alteration of an individual's lifestyle and the adoption of healthy behaviours have been shown to be effective in combating a majority of the major symptoms of depression. While there are several mHealth applications that use behaviour change to reduce depressive symptoms, they are often not grounded in behaviour change theory and display a lack of understanding regarding the specific factors that lead to behaviour change. Therefore, there is a need to test innovative strategies to tackle the issue. One such strategy is the use of behaviour change theories to design mHealth applications that use persuasive prompts to assist in 'prompting' the user to adopt healthier behaviours. In this paper, we present the design of persuasive prompts and an mHealth application to induce behavioural change, followed by the results of two studies: (i) an acceptability study that tested the acceptability of the persuasive prompts and (ii) a usability study of the mHealth application. The results show that there is a significant difference in which behaviour change theories users prefer based on their level of depression and that overall, users have a positive perception of the application. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Psychiatry & Clinical Psychology / Psychiatria i Psychologia Kliniczna is the property of Medical Communications Sp. z o.o. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The ICD-10 Classification of Mental and Behavioural Disorders introduced the category of 'acute and transient psychotic disorders' (ATPDs) encompassing polymorphic, schizophrenic and predominantly delusional subtypes, and the forthcoming ICD-11 revision has restricted it to polymorphic psychotic disorder, while the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) listed 'brief psychotic disorder' (BPD). To assess the predictive validity and outcome of ATPDs and BPD, relevant papers in English, French and German were searched in PubMed and Web of Science. Where possible meta-analysis of prognostic validators (diagnostic stability, course, outcome and response to treatment) was conducted. Fifty studies published between January 1993 and July 2019 were found. The clinical and functional outcome of ATPDs proved better than in schizophrenia and related disorders, but mortality risk is high, particularly suicide, and treatment trials provide little evidence. Meta-analysis of 25 studies (13,507 cases) revealed that 55% (95% CI 49–62) do not change diagnosis, 25% (95% CI 20–31) converted into schizophrenia and related disorders, and 12% (95% CI 7–16) into affective disorders on average over 6.3 years. Subgroup meta-analysis estimated prospective consistency of polymorphic psychotic disorder (55%; 95% CI 52–58) significantly greater than that of the ATPD subtypes with schizophrenic (OR 1.7; 95% CI 1.4–2.0) and predominantly delusional (OR 1.3; 95% CI 1.1–1.5) symptoms. Moreover, the diagnostic stability of BPD (13 studies; 294 cases) was 45% (95% CI 32–50) over a mean 4.2 years. Although these findings indicate that short-lived psychotic disorders have little predictive validity, significant differences among the ATPD subtypes support the revised ICD-11 ATPD category. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Turkish Sleep Medicine is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present study investigates the role of Taiwanese psychiatrists in turning neurasthenia into a culture-specific disease in the late twentieth century. It first delineates the shift in both explanatory models of psychoneuroses and patient population in post-World War II Taiwan. Neurasthenia became a focus of international attention in the 1970s and 1980s with the advance of cultural psychiatry, and, as China was closed to the outside world, Taiwanese psychiatrists were influential in framing the cultural meaning of neurasthenia. With the rise of post-socialist China, Taiwan lost its status as a key laboratory of Chinese studies. This paper argues that the history of neurasthenia during the period was closely associated with the professional development and national identity of Taiwanese psychiatrists. [ABSTRACT FROM AUTHOR]

Mental health disorders pose a significant global public health challenge. Social media data provides insights into these conditions. Analysing text can help identify indications of mental health disorders through text-based analysis. However, despite the large number of studies on the analysis of mental health disorders, the predominant algorithm in the existing literature is the Multi-Class Single-Level (MCSL) classification algorithm, which is often used for simple classification tasks involving a limited number of classes. Typically, these classes are binary, representing either an unhealthy or a healthy mental state. This paper uses English text data from Reddit to classify mental health disorders. The Multi-Class Multi-Level (MCML) classification algorithm was applied to perform detailed classification and address the limitations of the research scope using several approaches, including machine learning, deep learning, and transfer learning approaches. Two different pre-processing scenarios were proposed to handle unstructured text data, one of the most challenging aspects of classifying text from social media. The results of the experiments show that the MCML classification algorithm successfully performs detailed classification and produces promising results for each classification level. The proposed pre-processing scenario influences the performance of each classifier and improves classification accuracy. The best accuracy results were obtained for the Robustly Optimised BERT Pre-training Approach (RoBERTa) classifier at level 1 and level 2 classifications, namely 0.98 and 0.85, respectively. Overall, the MCML classification algorithm is proven to be used as a benchmark for early detection of text-based mental health disorders. [ABSTRACT FROM AUTHOR]

This paper examines the role of modality resources (e.g. "may," "often") in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) in representing behavioral pathology focusing, in particular, on Attention Deficit Hyperactivity Disorder (ADHD). ADHD diagnosis requires reports of non-practitioners (e.g. carers and teachers); an effective understanding of behavioral descriptors by the lay community is thus of paramount importance. The study combines qualitative linguistic discourse analysis and a corpus approach to study the presence and functions of modality, adopting a Systemic Functional perspective toward language. The study argues that in the DSM-5 modality is an important linguistic resource for conveying clinical significance, inferred from graduations of recurrence and probability. However, adopting features of professional discourse in representing behavioral pathology for non-experts, especially when those resources are inherently evaluative, stresses the need of health literacy among the lay social community and accessibility in health communication materials, particularly when non-practitioners are involved in the diagnosis practice. [ABSTRACT FROM AUTHOR]

Three IRT diagnostic-classification-modeling (DCM)-based multiple choice (MC) item design principles are stated that improve classroom quiz student diagnostic classification. Using proven-optimal maximum likelihood-based student classification, example items demonstrate that adherence to these item design principles increases attribute (skills and especially misconceptions) correct classification rates (CCRs). Simple formulas compute these needed item CCRs. By use of these psychometrically driven item design principles, hopefully enough attributes can be accurately diagnosed by necessarily short MC-item-based quizzes to be widely instructionally useful. These results should then stimulate increased use of well-designed MC item quizzes that target accurately diagnosing skills/misconceptions, thereby enhancing classroom learning. [ABSTRACT FROM AUTHOR]

Background: Major depressive disorder (MDD) is a leading cause of morbidity and mortality. Shortfalls in treatment quantity and quality are well-established, but the specific gaps in pharmacotherapy and psychotherapy are poorly understood. This paper analyzes the gap in treatment coverage for MDD and identifies critical bottlenecks. Methods: Seventeen surveys were conducted across 15 countries by the World Health Organization-World Mental Health Surveys Initiative. Of 35 012 respondents, 3341 met DSM-IV criteria for 12-month MDD. The following components of effective treatment coverage were analyzed: (a) any mental health service utilization; (b) adequate pharmacotherapy; (c) adequate psychotherapy; and (d) adequate severity-specific combination of both. Results: MDD prevalence was 4.8% (s.e., 0.2). A total of 41.8% (s.e., 1.1) received any mental health services, 23.2% (s.e., 1.5) of which was deemed effective. This 90% gap in effective treatment is due to lack of utilization (58%) and inadequate quality or adherence (32%). Critical bottlenecks are underutilization of psychotherapy (26 percentage-points reduction in coverage), underutilization of psychopharmacology (13-point reduction), inadequate physician monitoring (13-point reduction), and inadequate drug-type (10-point reduction). High-income countries double low-income countries in any mental health service utilization, adequate pharmacotherapy, adequate psychotherapy, and adequate combination of both. Severe cases are more likely than mild-moderate cases to receive either adequate pharmacotherapy or psychotherapy, but less likely to receive an adequate combination. Conclusions: Decision-makers need to increase the utilization and quality of pharmacotherapy and psychotherapy. Innovations such as telehealth for training and supervision plus non-specialist or community resources to deliver pharmacotherapy and psychotherapy could address these bottlenecks. [ABSTRACT FROM AUTHOR]