Übersichtsarbeiten / Review Articles. Was wollen wir (diagnostizieren) und was sollten wir diagnostizieren. Ein systemischer Überblick und Ausblick / What Would we Like to (Diagnose) and what do we have to Diagnose. A Systemic Overview and Perspective.

What Would we Like to (Diagnose) and what do we have to Diagnose. A Systemic Overview and Perspective Behavioral problems in children and young people are currently quickly suspected of being a sign of a psychological problem or a psychiatric disorder. In many cases, the caregivers agree on this, but in others there are clearly different perspectives from obvious to possible diagnoses. Whether a diagnosis makes sense and whether appropriate diagnostics are effective depends on various factors. From a systemic perspective, the common process, the symptomatic patients and their environment benefit from a solution- and resource-oriented view, which should have a greater impact on the future design of classifications of health and illness.

Reliability and validity of the Chinese version of the Benefit Finding Questionnaire for people with mental disorders.

BACKGROUND: Benefit finding (BF) is correlated with mental health and recovery, and its presence will contribute to the recovery of patients with mental disorders. Most of the current tools for assessing BF in patients with somatic disorders are not adequate for patients with mental disorders. The present study proposes to introduce the Benefit Finding Questionnaire for People with Mental Disorders and to validate its psychometric properties. METHODS: The Beaton translation model was used to translate and cross-culturally adjust the Japanese version of the Benefit Finding Questionnaire for People with Mental Disorders. A survey of 514 people with mental disorders was conducted from January 2022 to October 2022 using a general information questionnaire and a translated Chinese version of the Benefit Finding Questionnaire for People with Mental Disorders (BFQ-C) using a convenience sampling method. The quality of the questionnaire was examined in terms of item analysis, reliability, and validity. RESULTS: The results of the item analysis showed that all items met the requirements. The interrater agreement of the BFQ-C was good, with an interrater agreement = 0.714; the values of the item-level content validity index ranged from 0.75 to 1.00; and the average of all item-level content validity index on the scale = 0.958. Exploratory factor analysis extracted three main factors "change in relationship with others," "change in spirituality," and "change in values and thinking styles"-and the cumulative variance contribution rate was 57.70%. The results of the confirmatory factor analysis were χ2/df of 2.194, Root Mean Square Error of Approximation of 0.075, and comparative fit index of 0.919, indicating that the model fitted well. The questionnaire had a Cronbach' alpha of 0.936, a split reliability of 0.956, and a retest reliability of 0.939. CONCLUSION: The BFQ-C demonstrated good reliability and validity, and can be used to assess the BF level of people with mental disorders (e.g., anxiety disorders, depressive disorders, schizophrenia, and bipolar disorders) in China.

Nurses' experiences on the use of Positive Behaviour Support in the management of challenging behaviour in adults with a dual diagnosis of a mental health disorder and an intellectual disability.

INTRODUCTION: Individuals who have a dual diagnosis of both a psychiatric disorder and an intellectual disability (ID) are more likely to exhibit challenging behaviour than the general population. Clinicians globally have been encouraged to use positive approaches such as Positive Behaviour Support (PBS) when managing challenging behaviour. AIM: To explore nurses' views, opinions and perceptions on the use of positive behaviour support, as an adjunctive therapy, in the management of challenging behaviour in adults with a dual diagnosis of a mental health disorder and an intellectual disability within a mental health setting. METHOD: A descriptive qualitative study was undertaken to identify registered nurses' experiences of using PBS in managing challenging behaviour. Data were collected from ten participants via semi-structured interviews and analysed using thematic analysis. RESULTS: Two themes were constructed; 1) Being involved from the beginning and 2) Impact on adults with a dual diagnosis. DISCUSSION: Nurses' involvement from the onset was fundamental in maximising the potential of PBS. Benefits of PBS were identified. Having a meaningful relationship with clients and a good knowledge of their behaviours was integral to the success of PBS. IMPLICATIONS FOR PRACTICE: Participants emphasised the importance of continuous education around PBS. Nurses should be included in the formulation of PBS plans. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: RELEVANCE STATEMENT.

Assessment of psychometric properties of the union physio-psycho-social assessment questionnaire (UPPSAQ-70) in a large sample of general hospital psychiatric outpatients.

OBJECTIVE: To evaluate the psychometric properties of the Union Physio-Psycho-Social Assessment Questionnaire (UPPSAQ-70) among general hospital psychiatric outpatients. METHODS: A total of 2000 participants responded to the survey. Factor analyses were used to test the construct validity of the scale. Convergent validity was evaluated by the correlation between UPPSAQ-70 and symptoms measured using the Chinese versions of Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Patient Health Questionnaire-15 (PHQ-15), Somatic Symptom Disorder - B Criteria Scale (SSD-12) and Pittsburgh Sleep Quality Index (PSQI). RESULTS: The nine-factor model was supported (χ2 = 8816.395, df = 2309, χ2/df = 3.818, RMSEA = 0.053, CFI = 0.929). The UPPSAQ-70 showed significant correlation with the SAS (r = 0.396, P < .001), SDS (r = 0.451, P < .001), PHQ-15 (r = 0.381, P < .001), SSD-12 (r = 0.324, P < .001) and PSQI (r = 0.220, P < .001). UPPSAQ-70 and its subscales showed good internal consistency with Cronbach's alpha coefficients ranging from 0.79 to 0.96. CONCLUSIONS: The UPPSAQ-70 was a rating scale with good construct validity and reliability, which can measure overall health in the biological, psychological, and social domains for Chinese psychiatric outpatients, but its convergent validity still requires further empirical research.

An examination of relationships between transdiagnostic psychological processes and mental health disorders in athletes.

INTRODUCTION: The aims of this study were (a) to identify links between transdiagnostic psychological processes and mental health disorder (MHD) symptoms, and (b) to examine differences in MHD and transdiagnostic psychological processes as a function of demographic variables, including, gender, status as an athlete (elite vs. non-elite), number of training sessions per week, previous severe injuries, and use of medical care following severe injuries. METHODS: A total of 159 competitive athletes aged between 18 and 40 years old (44 % female; mean age = 24.20 ± 4.88 years) participated in this cross-sectional study. Participants completed a demographic questionnaire along with validated questionnaires evaluating MHD symptoms and transdiagnostic processes including: motivation to practice sport, emotional competencies, self-efficacy, repetitive negative thinking, meta-cognitive beliefs, and impulsive and perfectionist behaviors. RESULTS: MHD symptoms were positively correlated with controlled forms of motivation, repetitive negative thinking, meta-cognitive beliefs, and impulsive and perfectionistic behaviors; and negatively correlated with emotional competencies and self-efficacy. Gender differences showed that women were more likely to experience MHD, higher levels of repetitive negative thinking, meta-cognitive beliefs, and lower levels of emotional competencies and self-efficacy than men. Finally, elite athletes showed higher personal standards than their non-elite counterparts and those training more than four times per week showed significantly higher perfectionistic behaviors. DISCUSSION: These results are in line with previous findings in clinical psychology and shed light on the role of transdiagnostic processes and the risk of MHD in a sample of French athletes. Further research on the identification of key risk factors for MHD in competitive athletes is needed.

Übersichtsarbeiten / Review Articles. Voreingenommenheiten und Wahrnehmungsverzerrungen (Bias) in der psychiatrisch-psychotherapeutischen Versorgung von Kindern und Jugendlichen mit intellektueller Beeinträchtigung.

Bias in Mental Health Care of Children and Adolescents with Intellectual Disabilities Implicit and explicit bias and distortions of perception are partly responsible for the unequal and significantly deficient psychotherapeutic and psychiatric care situation for children and adolescents with intellectual disabilities and additional behavioral problems. The extent to which these biases influence misdiagnoses and treatment errors, refusals and exclusions from professional care, and grossly hostile rejections of people with disabilities requires empirical evidence (Bartig et al., 2021). The fact that all forms occur - probably to a considerable extent - contradicts the ethical principles of the medical and psychotherapeutic profession. In order to avoid misdiagnosis and treatment as a result of bias, this must be openly addressed. Selfawareness, supervision and second views, the concept of working diagnosis and, above all, the full application of child and adolescent psychiatric standards help to reduce bias.

Screening instruments for mental disorders in primary healthcare: a scoping review protocol (SCREENING-MD).

INTRODUCTION: When mental disorders go undetected until later stages, they can result in poorer health outcomes for patients. Primary healthcare (PHC) stands as a strategic setting for the early identification and management of these mental disorders, given its role as the primary care environment for health service users. This scoping review has the objective of mapping and assessing screening instruments validated for mental disorders that are applicable in PHC, particularly regarding their measurement properties. METHODS AND ANALYSIS: This scoping review will include studies that have developed and validated screening instruments for mental disorders in the PHC context, irrespective of the age group. Searches will be conducted in MEDLINE, EMBASE, LILACS, CINAHL and PsycInfo without imposing restrictions on publication status, publication year or language. Additionally, we will scrutinise the references cited in the selected studies. Our inclusion criteria encompass studies examining any measurement property recommended by the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) taxonomy. The selection process, data extraction and quality assessment of studies will be performed independently by pairs of reviewers. To evaluate the risk of bias within the selected studies, we will employ the COSMIN Risk of Bias 2 tools. The collected data will undergo analysis using descriptive statistics and will be presented in an evidence gap map format for each specific mental disorder. ETHICS AND DISSEMINATION: The findings from this review will be discussed through deliberative dialogue with stakeholders and disseminated through peer-reviewed publications and conference presentations. The project was approved by the Ethics Committee for Research at the University of Sorocaba (number: 66993323.9.0000.5500). TRIAL REGISTRATION NUMBER: Open Science Framework - 10.17605/OSF.IO/Z6T5M.

Patient Perspectives on AI for Mental Health Care: Cross-Sectional Survey Study.

BACKGROUND: The application of artificial intelligence (AI) to health and health care is rapidly increasing. Several studies have assessed the attitudes of health professionals, but far fewer studies have explored the perspectives of patients or the general public. Studies investigating patient perspectives have focused on somatic issues, including those related to radiology, perinatal health, and general applications. Patient feedback has been elicited in the development of specific mental health care solutions, but broader perspectives toward AI for mental health care have been underexplored. OBJECTIVE: This study aims to understand public perceptions regarding potential benefits of AI, concerns about AI, comfort with AI accomplishing various tasks, and values related to AI, all pertaining to mental health care. METHODS: We conducted a 1-time cross-sectional survey with a nationally representative sample of 500 US-based adults. Participants provided structured responses on their perceived benefits, concerns, comfort, and values regarding AI for mental health care. They could also add free-text responses to elaborate on their concerns and values. RESULTS: A plurality of participants (245/497, 49.3%) believed AI may be beneficial for mental health care, but this perspective differed based on sociodemographic variables (all P<.05). Specifically, Black participants (odds ratio [OR] 1.76, 95% CI 1.03-3.05) and those with lower health literacy (OR 2.16, 95% CI 1.29-3.78) perceived AI to be more beneficial, and women (OR 0.68, 95% CI 0.46-0.99) perceived AI to be less beneficial. Participants endorsed concerns about accuracy, possible unintended consequences such as misdiagnosis, the confidentiality of their information, and the loss of connection with their health professional when AI is used for mental health care. A majority of participants (80.4%, 402/500) valued being able to understand individual factors driving their risk, confidentiality, and autonomy as it pertained to the use of AI for their mental health. When asked who was responsible for the misdiagnosis of mental health conditions using AI, 81.6% (408/500) of participants found the health professional to be responsible. Qualitative results revealed similar concerns related to the accuracy of AI and how its use may impact the confidentiality of patients' information. CONCLUSIONS: Future work involving the use of AI for mental health care should investigate strategies for conveying the level of AI's accuracy, factors that drive patients' mental health risks, and how data are used confidentially so that patients can determine with their health professionals when AI may be beneficial. It will also be important in a mental health care context to ensure the patient-health professional relationship is preserved when AI is used.

Portable polygraphic device (Somnocheck micro CARDIO®) provides accurate diagnostic information in psychiatric patients at risk for obstructive sleep apnoea: an observational cohort study.

BACKGROUND: Symptoms of obstructive sleep apnoea (OSA) overlap significantly with those of psychiatric disorders, making accurate diagnosis of OSA challenging within psychiatric settings. Diagnosing OSA in psychiatric patients is crucial because untreated OSA can exacerbate psychiatric symptoms, reduce treatment efficacy, and impair overall quality of life. This study aimed to determine the diagnostic accuracy of a readily accessible procedure for psychiatric patients in a real-world clinical setting by comparing the Somnocheck micro CARDIO® (SCm) portable cardiorespiratory polygraphy device with the gold standard polysomnography (PSG). METHODS: This observational cohort study included consecutive psychiatric patients at intermediate to high risk for OSA based on screening with the STOP-Bang questionnaire, admitted to a single tertiary care centre between June 1, 2016 and December 31, 2022. The Apnoea-Hypopnoea-Index (AHI), Apnoea-Index (AI), Oxygen-Desaturation-Index (ODI), and minimum oxygen saturation were measured sequentially by SCm and PSG. RESULTS: A total of 57 patients were analysed (median age 62.0 [Interquartile Range (IQR), 51.5-72.5] years; 34 [59.6%] men). Regarding AHI, no significant differences (AHI measured by PSG, median, 16.6 [IQR, 6.2-26.7] vs. AHI measured by SCm, median, 14.9 [IQR, 10.0-22.8]; p = 0.812; r = 0.71) were found between SCm and PSG. AI, ODI and minimum oxygen saturation differed significantly between SCm and PSG. Using optimised cut-off values (any OSA: AHISCm ≥ 9.25), SCm showed high sensitivity (0.894) and high specificity (0.800) for the diagnosis of OSA, with an area under the receiver operating characteristic curve of 0.877. CONCLUSIONS: This study found that the SCm portable device was accurate in identifying psychiatric patients with OSA. AHI measurement by SCm provided reliable diagnostic performance in comparison with the gold standard polysomnography. These findings support the integration of polygraphic measurements into the routine sleep assessment of psychiatric patients. Early and accurate diagnosis of OSA in this population can significantly improve the management of both sleep disorders and psychiatric conditions, potentially enhancing overall treatment outcomes and quality of life for these patients.

Digital Drawing Tools for Assessing Mental Health Conditions - A Scoping Review.

INTRODUCTION: Drawing tasks are an elementary component of psychological assessment in the evaluation of mental health. With the rise of digitalization not only in psychology but healthcare in general, digital drawing tools (dDTs) have also been developed for this purpose. This scoping review aims at summarizing the state of the art of dDTs available to assess mental health conditions in people above preschool age. METHODS: PubMed, PsycInfo, PsycArticles, CINAHL, and Psychology and Behavioral Sciences Collection were searched for dDTs from 2000 onwards. The focus was on dDTs, which not only evaluate the final drawing, but also process data. RESULTS: After applying the search and selection strategy, a total of 37 articles, comprising unique dDTs, remained for data extraction. Around 75 % of these articles were published after 2014 and most of them target adults (86.5 %). In addition, dDTs were mainly used in two areas: tremor detection and assessment of cognitive states, utilizing, for example, the Spiral Drawing Test and the Clock Drawing Test. CONCLUSION: Early detection of mental diseases is an increasingly important field in healthcare. Through the integration of digital and art-based solutions, this area could expand into an interdisciplinary science. This review shows that the first steps in this direction have already been taken and that the possibilities for further research, e.g., on the optimized application of dDTs, are still open.

How to distinguish promotion, prevention and treatment trials in public mental health? Study protocol for the development of the VErona-LUgano Tool (VELUT).

INTRODUCTION: Promoting mental health, preventing and treating mental disorders are critically important in public health, and many randomised controlled trials (RCTs) evaluate intervention strategies for these objectives. However, distinguishing promotion from prevention and from treatment RCTs is challenging. A tool to place studies along the promotion-to-treatment continuum in mental health research does not exist, leaving it to researchers and policymakers to decide on how to classify individual RCTs, which hinders evidence synthesis. METHODS AND ANALYSIS: We present a protocol for the development of a new tool to assist researchers in distinguishing RCTs along the promotion-to-treatment continuum. We will establish a Tool Development Group, and use the Population, Intervention, Comparison and Outcome framework to define constructs. We will generate, define, categorise and reduce the items in the tool using qualitative methods, including cognitive interviews and a Delphi exercise. Psychometric evaluation-including unidimensionality, local independence, monotonicity and item homogeneity-will include data collection, scoring, internal consistency checks and factor analysis of the tool's indicators for available RCTs. We will use standard Cohen's kappa statistics to assess the reliability of the tool. ETHICS AND DISSEMINATION: This study involves data collection from the already published literature. However, this protocol has been approved by the ethics committee of the Università della Svizzera Italiana (CE 2024 04). The results of the present project will be disseminated in peer-reviewed journals and at international and national scientific meetings. Training materials for the application of the tool will also be developed and disseminated to the scientific community. The tool and all related implementation materials will be published on a website and will be freely accessible to the public.

Application of Facial Expression Analysis Technology in Violence Risk Assessment of Individuals with Mental Disorders in Supervised Settings.

OBJECTIVES: To explore the association between violent behaviors and emotions in individuals with mental disorders, to evaluate the application value of facial expression analysis technology in violence risk assessment of individuals with mental disorders in supervised settings, and to provide a reference for violence risk assessment. METHODS: Thirty-nine male individuals with mental disorders in supervised settings were selected, the participant risk of violence, cognitive function, psychiatric symptoms and severity were assessed using the Modified Overt Aggression Scale (MOAS), the Historical, Clinical, Risk Management-Chinese version(HCR-CV), the Positive and Negative Syndrome Scale (PANSS) and the Brief Psychiatric Rating Scale (BPRS). An emotional arousal was performed on the participants and the intensity of their emotions and facial expression action units was recorded before, during and after the arousal. One-way analysis of variance (ANOVA) was used to compare the differences in the intensity of emotions and facial expression action units before, during and after the arousal. Pearson correlation analysis was used to calculate the correlations between the intensity of the seven basic emotional facial expressions and the scores of the assessment scales. RESULTS: The intensity difference of sadness, surprise and fear in different time periods was statistically significant (P<0.05). The intensity of the left medial eyebrow lift action unit was found significantly different before and after the emotional arousal (P<0.05). The intensity of anger was positively correlated with the Modified Overt Aggression Scale score throughout the experiment (P<0.05). CONCLUSIONS: Eye action units such as eyebrow lifting, eyelid tightening and upper eyelid lifting can be used as effective action units to identify sadness, anger and other negative emotions associated with violent behaviors. Facial expression analysis technology can be used as an auxiliary tool to assess the potential risk of violence in individuals with mental disorders in supervised settings.

Interventions targeting patients with co-occuring severe mental illness and substance use (dual diagnosis) in general practice settings - a scoping review of the literature.

BACKGROUND: People with dual diagnosis die prematurely compared to the general population, and general practice might serve as a setting in the healthcare system to mend this gap in health inequity. However, little is known about which interventions that have been tested in this setting. AIM: To scope the literature on interventions targeting patients with dual diagnosis in a general practice setting, the outcomes used, and the findings. DESIGN AND SETTING: A scoping review of patients with dual diagnosis in general practice. METHODS: From a predeveloped search string, we used PubMed (Medline), PsychInfo, and Embase to identify scientific articles on interventions. Studies were excluded if they did not evaluate an intervention, if patients were under 18 years of age, and if not published in English. Duplicates were removed and all articles were initially screened by title and abstract and subsequent fulltext were read by two authors. Conflicts were discussed within the author group. A summative synthesis of the findings was performed to present the results. RESULTS: Seven articles were included in the analysis. Most studies investigated integrated care models between behavioural treatment and primary care, and a single study investigated the delivery of Cognitive Behavioral treatment (CBT). Outcomes were changes in mental illness scores and substance or alcohol use, treatment utilization, and implementation of the intervention in question. No studies revealed significant outcomes for patients with dual diagnosis. CONCLUSION: Few intervention studies targeting patients with dual diagnosis exist in general practice. This calls for further investigation of the possibilities of implementing interventions targeting this patient group in general practice.

Examining the role of personality functioning in a hierarchical taxonomy of psychopathology using two years of ambulatory assessed data.

The Hierarchical Taxonomy of Psychopathology (HiTOP) arranges phenotypes of mental disorders based on empirical covariation, ranging from narrowly defined symptoms to higher-order spectra of psychopathology. Since the introduction of personality functioning (PF) in DSM-5 and ICD-11, several studies have identified PF as a predictor of transdiagnostic aspects of psychopathology. However, the role of PF in the HiTOP classification system has not been systematically examined. This study investigates how PF can be integrated into HiTOP, whether PF accounts for transdiagnostic variance captured in higher-order spectra, and how its predictive value for future affective well-being (AWB) and psychosocial impairment (PSI) compares to the predictive value of specific psychopathology beyond PF. To this end, we examined two years of ambulatory assessed data on psychopathology, PF, PSI, and AWB of N = 27,173 users of a mental health app. Results of bass-ackwards analyses largely aligned with the current HiTOP working model. Using bifactor modeling, aspects of PF were identified to capture most of the internalizing, thought disorder, and externalizing higher-order factor variance. In longitudinal prediction analyses employing bifactor-(S-1) modeling, PF explained 58.6% and 30.6% of variance in PSI and AWB when assessed across one year, respectively, and 33.1% and 23.2% of variance when assessed across two years. Results indicate that personality functioning may largely account for transdiagnostic variance captured in the higher-order components in HiTOP as well as longitudinal outcomes of PSI and AWB. Clinicians and their patients may benefit from assessing PF aspects such as identity problems or internal relationship models in a broad range of mental disorders. Further, incorporating measures of PF may advance research in biological psychiatry by providing empirically sound phenotypes.

Using joint probability density to create most informative unidimensional indices: a new method using pain and psychiatric severity as examples.

BACKGROUND: Dimension reduction methods do not always reduce their underlying indicators to a single composite score. Furthermore, such methods are usually based on optimality criteria that require discarding some information. We suggest, under some conditions, to use the joint probability density function (joint pdf or JPD) of p-dimensional random variable (the p indicators), as an index or a composite score. It is proved that this index is more informative than any alternative composite score. In two examples, we compare the JPD index with some alternatives constructed from traditional methods. METHODS: We develop a probabilistic unsupervised dimension reduction method based on the probability density of multivariate data. We show that the conditional distribution of the variables given JPD is uniform, implying that the JPD is the most informative scalar summary under the most common notions of information. B. We show under some widely plausible conditions, JPD can be used as an index. To use JPD as an index, in addition to having a plausible interpretation, all the random variables should have approximately the same direction(unidirectionality) as the density values (codirectionality). We applied these ideas to two data sets: first, on the 7 Brief Pain Inventory Interference scale (BPI-I) items obtained from 8,889 US Veterans with chronic pain and, second, on a novel measure based on administrative data for 912 US Veterans. To estimate the JPD in both examples, among the available JPD estimation methods, we used its conditional specifications, identified a well-fitted parametric model for each factored conditional (regression) specification, and, by maximizing the corresponding likelihoods, estimated their parameters. Due to the non-uniqueness of conditional specification, the average of all estimated conditional specifications was used as the final estimate. Since a prevalent common use of indices is ranking, we used measures of monotone dependence [e.g., Spearman's rank correlation (rho)] to assess the strength of unidirectionality and co-directionality. Finally, we cross-validate the JPD score against variance-covariance-based scores (factor scores in unidimensional models), and the "person's parameter" estimates of (Generalized) Partial Credit and Graded Response IRT models. We used Pearson Divergence as a measure of information and Shannon's entropy to compare uncertainties (informativeness) in these alternative scores. RESULTS: An unsupervised dimension reduction was developed based on the joint probability density (JPD) of the multi-dimensional data. The JPD, under regularity conditions, may be used as an index. For the well-established Brief Pain Interference Inventory (BPI-I (the short form with 7 Items) and for a new mental health severity index (MoPSI) with 6 indicators, we estimated the JPD scoring. We compared, assuming unidimensionality, factor scores, Person's scores of the Partial Credit model, the Generalized Partial Credit model, and the Graded Response model with JPD scoring. As expected, all scores' rankings in both examples were monotonically dependent with various strengths. Shannon entropy was the smallest for JPD scores. Pearson Divergence of the estimated densities of different indices against uniform distribution was maximum for JPD scoring. CONCLUSIONS: An unsupervised probabilistic dimension reduction is possible. When appropriate, the joint probability density function can be used as the most informative index. Model specification and estimation and steps to implement the scoring were demonstrated. As expected, when the required assumption in factor analysis and IRT models are satisfied, JPD scoring agrees with these established scores. However, when these assumptions are violated, JPD scores preserve all the information in the indicators with minimal assumption.

Visualizing Mental Health Insights: A Pipeline from Social Media to Chernoff Faces.

This study proposes an approach for analyzing mental health through publicly available social media data, employing Large Language Models (LLMs) and visualization techniques to transform textual data into Chernoff Faces. The analysis began with a dataset comprising 15,744 posts sourced from major social media platforms, which was refined down to 2,621 posts through meticulous data cleaning, feature extraction, and visualization processes. Our methodology includes stages of Data Preparation, Feature Extraction, Chernoff Face Visualization, and Clinical Validation. Dimensionality reduction techniques such as PCA, t-SNE, and UMAP were employed to transform complex mental health data into comprehensible visual representations. Validation involved a survey among 60 volunteer psychiatrists, underscoring the visualizations' potential for enhancing clinical assessments. This work sets the stage for future evaluations, specifically focusing on a combined features method to further refine the visual representation of mental health conditions and to augment the diagnostic tools available to mental health professionals.

Exploring the return-on-investment for scaling screening and psychosocial treatment for women with common perinatal mental health problems in Malawi: Developing a cost-benefit-calculator tool.

This study sought to develop a user-friendly decision-making tool to explore country-specific estimates for costs and economic consequences of different options for scaling screening and psychosocial interventions for women with common perinatal mental health problems in Malawi. We developed a simple simulation model using a structure and parameter estimates that were established iteratively with experts, based on published trials, international databases and resources, statistical data, best practice guidance and intervention manuals. The model projects annual costs and returns to investment from 2022 to 2026. The study perspective is societal, including health expenditure and productivity losses. Outcomes in the form of health-related quality of life are measured in Disability Adjusted Life Years, which were converted into monetary values. Economic consequences include those that occur in the year in which the intervention takes place. Results suggest that the net benefit is relatively small at the beginning but increases over time as learning effects lead to a higher number of women being identified and receiving (cost­)effective treatment. For a scenario in which screening is first provided by health professionals (such as midwives) and a second screening and the intervention are provided by trained and supervised volunteers to equal proportions in group and individual sessions, as well as in clinic versus community setting, total costs in 2022 amount to US$ 0.66 million and health benefits to US$ 0.36 million. Costs increase to US$ 1.03 million and health benefits to US$ 0.93 million in 2026. Net benefits increase from US$ 35,000 in 2022 to US$ 0.52 million in 2026, and return-on-investment ratios from 1.05 to 1.45. Results from sensitivity analysis suggest that positive net benefit results are highly sensitive to an increase in staff salaries. This study demonstrates the feasibility of developing an economic decision-making tool that can be used by local policy makers and influencers to inform investments in maternal mental health.

Development of an eye-tracking system based on a deep learning model to assess executive function in patients with mental illnesses.

Patients with mental illnesses, particularly psychosis and obsessive‒compulsive disorder (OCD), frequently exhibit deficits in executive function and visuospatial memory. Traditional assessments, such as the Rey‒Osterrieth Complex Figure Test (RCFT), performed in clinical settings require time and effort. This study aimed to develop a deep learning model using the RCFT and based on eye tracking to detect impaired executive function during visuospatial memory encoding in patients with mental illnesses. In 96 patients with first-episode psychosis, 49 with clinical high risk for psychosis, 104 with OCD, and 159 healthy controls, eye movements were recorded during a 3-min RCFT figure memorization task, and organization and immediate recall scores were obtained. These scores, along with the fixation points indicating eye-focused locations in the figure, were used to train a Long Short-Term Memory + Attention model for detecting impaired executive function and visuospatial memory. The model distinguished between normal and impaired executive function, with an F1 score of 83.5%, and identified visuospatial memory deficits, with an F1 score of 80.7%, regardless of psychiatric diagnosis. These findings suggest that this eye tracking-based deep learning model can directly and rapidly identify impaired executive function during visuospatial memory encoding, with potential applications in various psychiatric and neurological disorders.