Mental Health Care Treatment Research Articles

Health-related quality of life (HRQoL) loss associated with self-perceived anxiety/depression in seropositive rheumatoid arthritis.

To analyze the HRQoL loss associated with self-perceived anxiety/depression in patients with seropositive rheumatoid arthritis (RA). This secondary data analysis is based on a registry-based retrospective follow-up study of patients with seropositive RA treated between August 2014 and January 2023 in ARTMEDICA, Colombia. HRQoL loss and self-perceived anxiety/depression were defined as outcomes. Disease activity (DAS-28) and other patient data were also gathered. Statistical analyses were performed using the ordinal logistic and generalized linear regression models. A total of 3579 patients with a mean follow-up of 2.9 (SD 2.4) years, 85.6% women with a median age at diagnosis of 48.1 (IQR 37.8-57.5) years, and a median of 6.5 (IQR 1.9-14.7) years living with RA were included. At program admission, the median DAS-28 score was 2.8 (IQR 2.1-4.2), and 6.6% of patients reported extreme anxiety/depression. The average HRQoL loss was 3.4months per year lived with seropositive AR. Among patients with no pain or discomfort, moderate and extreme anxiety/depression were associated with mean HRQoL losses of 2.2 (95% CI - 2.3 to - 2.2) and 4.1 (95% CI - 4.3 to - 3.8) months. In patients with extreme pain/discomfort, these estimations were 0.8 (95% CI - 0.9 to - 0.7) and 1.9 (95% CI - 2.1 to - 1.7) months, respectively. Our study adds to the available body of evidence by clarifying the differential impact of anxiety/depression on HRQoL, depending on the severity of pain. These findings highlight the importance of strengthening mental health care and psychological well-being interventions for patients with RA, regardless of pain or disease activity. Key Points • The average HRQoL loss was 3.4months per year lived with seropositive AR. • Pain/discomfort rather than disease activity explained the severity of anxiety/depression as well as its associated HRQoL loss. • For patients with extreme pain/discomfort and anxiety/depression, the average HRQoL loss was 8.1months per year lived with the disease compared to 0.4months for patients without those impacts.

A Review of Chatbots Application on Supporting International Students’ Mental Health

International students face distinctive challenges, including acculturative stress, homesickness, language barriers, and heightened academic expectations. As a result, their mental health and overall well-being have become subjects of growing concern and significance in academic and support communities worldwide. The emergence of AI technologies, like ChatGPT, opens new avenues for international student mental health interventions. However, no specific Chatbot has been approved as effective for this population. This study aims to comprehensively summarize Chatbot applications in mental health counseling and analyze the challenges of applying them to international students. The study found that prominent Chatbots could demonstrate efficacy in reducing symptoms of depression and anxiety through techniques such as cognitive behavioral therapy (CBT). Chatbots are becoming integral to mental health care, providing accessible and evidence-based interventions and psychoeducation. Compared with traditional in-personal mental health counselling, a Chatbot is more functioning in providing personalized therapy sessions, essay accessibility, real-time responses, and suitable assistance, which could solve current challenges and unmet mental health counselling needs of international students. Despite their potential, ethical considerations and privacy challenges must be addressed to ensure reliable and secure use. Continued refinement is needed for international students’ application to enhance multicultural sensitivity, the accuracy of cross-language interpretation, and personalization. The evolution of Chatbots holds significant promise for enhancing mental health support, particularly for international students navigating and coping with their unique challenges.

Gender identity differences in age of first mental health diagnosis: A population-based study of childhood and adolescence

Abstract Introduction Gender minority (GM; i.e., trans, non-binary, and other gender diverse) individuals have an increased risk of mental health disorders, but few studies have explored disparities using population-based samples or the age of onset of this risk. Objectives The aim of the current study was to identify the age at which the gender identity disparity in physician-assessed psychiatric diagnosis and treatment for common mental health disorders can be first identified in a population-based sample linked to national health care registries. Methods All individuals aged 16 to 25 (n = 18,793) participating in the nationwide probability-based Swedish National Public Health Survey in 2018, 2020, or 2021 were included in the analyses. This sample could be linked to mental health care treatment history data in nationally comprehensive health care registries starting at age 8 years old. Results Individuals reporting a GM identity in adulthood were seven times more likely to having been diagnosed with depression and/or anxiety and about five times more likely to having been diagnosed with a neurodevelopmental disorder during childhood or adolescence compared to individuals with a cisgender identity. GM individuals’ increased likelihood of being diagnosed with depression and/or anxiety was significantly elevated from age 13, while their neurodevelopmental disorder diagnosis risk was significantly elevated from age 10, compared to cisgender individuals. Conclusions The results from this study are the first population-based evidence to show that GM individuals are more likely to be diagnosed and treated for mental health disorders early during childhood and adolescence compared to cisgender individuals. The early emergence of this increased risk requires further inquiry into causes of these early disparities. It also suggests a potential benefit of interventions that facilitate social belonging and inclusion and reduce stressors related to societal expectations of gender.

Finding the Psychological Instability Using Machine Learning

Identifying the psychological instability in mental health assessment through the application of ML techniques, using the principally the RFA (Random Forest Algorithm). This research investigates the application of machine learning techniques to detect psychological instability in individuals. By employing a variety of algorithms, including both supervised and unsupervised learning methods, this study aims to predict psychological states based on diverse data inputs such as behavioural patterns, physiological signals, and social interactions. The models are developed and validated using datasets from clinical studies, social media activity, and wearable health devices. The results illustrate the capability of ML to provide accurate and timely predictions of psychological instability, offering valuable insights for early diagnosis and intervention in mental health care. This study advances the field by demonstrating a data-driven approach to understanding and managing psychological health.

Effect of the i2TransHealth e-health intervention on psychological distress among transgender and gender diverse adults from remote areas in Germany: a randomised controlled trial

Transgender and gender diverse (TGD) people in remote areas face challenges accessing health-care services, including mental health care and gender-affirming medical treatment, which can be associated with psychological distress. In this study, we aimed to evaluate the effectiveness of a 4-month TGD-informed e-health intervention to improve psychological distress among TGD people from remote areas in northern Germany. In a randomised controlled trial done at a single centre in Germany, adults (aged ≥18 years) who met criteria for gender incongruence or gender dysphoria and who lived at least 50 km outside of Hamburg in one of the northern German federal states were recruited and randomly assigned (1:1) to i2TransHealth intervention or a wait list control group. Randomisation was performed with the use of a computer-based code. Due to the nature of the intervention, study participants and clinical staff were aware of treatment allocation, but researchers responsible for data analysis were masked to allocation groups. Study participants in the intervention group (service users) started the i2TransHealth intervention immediately after completing the baseline survey after enrolment. Participants assigned to the control group waited 4 months before they were able to access i2TransHealth services or regular care. The primary outcome was difference in the Brief Symptom Inventory (BSI)-18 summary score between baseline and 4 months, assessed using a linear model analysis. The primary outcome was assessed in the intention-to-treat (ITT) population, which included all randomly assigned participants. The trial was registered with ClinicalTrials.gov, NCT04290286. Between May 12, 2020, and May 2, 2022, 177 TGD people were assessed for eligibility, of whom 174 were included in the ITT population (n=90 in the intervention group, n=84 in the control group). Six participants did not provide data for the primary outcome at 4 months, and thus 168 people were included in the analysis population (88 participants in the intervention group and 80 participants in the control group). At 4 months, in the intervention group, the adjusted mean change in BSI-18 from baseline was -0·65 (95% CI -2·25 to 0·96; p=0·43) compared with 2·34 (0·65 to 4·02; p=0·0069) in the control group. Linear model analysis identified a significant difference at 4 months between the groups with regard to change in BSI-18 summary scores from baseline (between-group difference -2·98 [95% CI -5·31 to -0·65]; p=0·012). Adverse events were rare: there were two suicide attempts and one participant was admitted to hospital in the intervention group, and in the control group, there was one case of self-harm and one case of self-harm followed by hospital admission. The intervention was clinically significant in averting worsening psychological distress in service users, outperforming the wait list control group. These findings support the effectiveness of e-health services in TGD health care, specifically for people from remote areas. Innovation Committee at the Federal Joint Committee. For the German translation of the abstract see Supplementary Materials section.

Trends and Disparities in Mental Health Use Among Asian American Sub-groups, 2013-2019.

The objective of this study was to characterize and compare national estimates of mental healthcare use among White and Asian American groups to provide an update using post Affordable Care Act data. We analyzed yearly cross-sectional data from the 2013-2019 Medical Expenditure Panel Survey, including White (n=112,590) and Asian American (n=10,210) individuals, and examined rates of mental healthcare use for Asian (overall), Asian Indian, Chinese, Filipino, and Other Asian individuals relative to White individuals. Using multivariable logistic regression models and predictive margin methods, we estimated overall Asian disparities and Asian subgroup disparities compared to White group rates in mental health care (outpatient, specialty, psychotropic medication) among adults with and without elevated risk for mental illness. Regression models were adjusted for variables related to need for treatment, demographic, and socioeconomic status variables. Asian individuals had lower rates of mental healthcare use than White individuals. Unadjusted results and adjusted regression model predictions are consistent in identifying wide disparities in mental health care treatment across risk for mental illness, Asian subgroups, and types of treatment. Asian Americans have significantly lower rates of mental healthcare use than White Americans, even among those with elevated risk for mental illness. There is small variation by Asian subgroups but disparities persist across subgroups and types of treatment. Our results imply interventions are needed to improve linguistically, culturally, and ethnically tailored outreach and engagement in treatment services, as well as examining treatment and its effectiveness for Asian American individuals living with psychological distress.

Using physical health apps to promote healthy lifestyles in youth mental healthcare: A nationwide perspective-gathering exercise of over 400 service users

Digital technologies have presented a myriad of new solutions for improving cardiometabolic and behavioural health in the general population. However, the ways in which such advances could be applied to address the heightened health-risk behaviours and associated diseases in mental healthcare is unknown. To examine this, 492 young people with mental illness (YPMI) were recruited from 27 Primary Care and NHS mental healthcare sites across the UK, covering various diagnoses (excluding eating disorders). Participants were presented with four types of physical health apps, delivering: 1) Health Tracking; 2) Health Coaching; 3) Health Connections; and 4) Instructional Videos, and completed an online perspective-gathering exercise on the preferred utility, features, behavioural targets of these technologies, and barriers/facilitators to uptake. Results showed a high level of perceived utility across each of the four app types, with physical activity, sleep and diet emerging as preferred behavioural targets. Feedback on ideal app features indicated a need for integrated physical-mental health tracking, and expert-led instructional content/coaching, with less interest expressed towards sharing data with clinical teams. These findings can improve the development, future trials, and clinical implementation of digital lifestyle interventions in mental healthcare, through better accounting for the needs and preferences of YPMI.

Predicting Mental Health Care Enrollment and Treatment Uptake among Newly Arrived Refugees in U.S. Resettlement Programs.

This article examines variables that predict mental health care enrollment and engagement among refugees. The authors explore a mental health care model designed to identify mental health needs early among refugee arrivals that may interfere with adjustment and overall health outcomes using data from a Midwest refugee resettlement program. Using ecological models of mental distress and theories on help-seeking behaviors, the authors used logistic and count regressions to predict enrollment in mental health care services and utilization rates. Gender, nationality, and presenting problems were significant predictors of enrollment and length of engagement in services. The findings provide guidance to practitioners and resettlement agencies on refugee mental health and opportunities to increasing access and engagement in mental health care.

Prolonged Grief, Posttraumatic Stress and Depression Symptoms Before and After COVID-19 in Brazil.

This study aims to explore the differences in prolonged grief (PDG), posttraumatic stress (PTSD), and depression symptoms (MDD) following the loss of a loved one before and after the COVID-19 pandemic. Using a cross-sectional design, 888 bereaved individuals were classified on different groups according to the cause of death. Results indicate that participants who experienced a loss during the pandemic, regardless of the cause, presented higher levels of prolonged grief, posttraumatic stress and depression when compared to those who lost a loved one before the pandemic. This study sheds light on the environmental influences (ex: the COVID-19 pandemic) on the grieving process, emphasizing the need to take the context into account when tailoring interventions in bereavement and mental health care.

Facial image analysis for automated suicide risk detection with deep neural networks

Accurately assessing suicide risk is a critical concern in mental health care. Traditional methods, which often rely on self-reporting and clinical interviews, are limited by their subjective nature and may overlook non-verbal cues. This study introduces an innovative approach to suicide risk assessment using facial image analysis. The Suicidal Visual Indicators Prediction (SVIP) Framework leverages EfficientNetb0 and ResNet architectures, enhanced through Bayesian optimization techniques, to detect nuanced facial expressions indicating mental state. The models’ interpretability is improved using GRADCAM, Occlusion Sensitivity, and LIME, which highlight significant facial regions for predictions. Using datasets DB1 and DB2, which consist of full and cropped facial images from social media profiles of individuals with known suicide outcomes, the method achieved 67.93% accuracy with EfficientNetb0 on DB1 and up to 76.6% accuracy with a Bayesian-optimized Support Vector Machine model using ResNet18 features on DB2. This approach provides a less intrusive, accessible alternative to video-based methods and demonstrates the substantial potential for early detection and intervention in mental health care.

Smartphone-Delivered Attentional Bias Modification Training for Mental Health: Systematic Review and Meta-Analysis.

Smartphone-delivered attentional bias modification training (ABMT) intervention has gained popularity as a remote solution for alleviating symptoms of mental health problems. However, the existing literature presents mixed results indicating both significant and insignificant effects of smartphone-delivered interventions. This systematic review and meta-analysis aims to assess the impact of smartphone-delivered ABMT on attentional bias and symptoms of mental health problems. Specifically, we examined different design approaches and methods of administration, focusing on common mental health issues, such as anxiety and depression, and design elements, including gamification and stimulus types. Our search spanned from 2014 to 2023 and encompassed 4 major databases: MEDLINE, PsycINFO, PubMed, and Scopus. Study selection, data extraction, and critical appraisal were performed independently by 3 authors using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. When necessary, we pooled the standardized mean difference with a 95% CI. In addition, we conducted sensitivity, subgroup, and meta-regression analyses to explore moderator variables of active and placebo ABMT interventions on reducing symptoms of mental health problems and attentional bias. Our review included 12 papers, involving a total of 24,503 participants, and we were able to conduct a meta-analysis on 20 different study samples from 11 papers. Active ABMT exhibited an effect size (Hedges g) of -0.18 (P=.03) in reducing symptoms of mental health problems, while the overall effect remained significant. Similarly, placebo ABMT showed an effect size of -0.38 (P=.008) in reducing symptoms of mental health problems. In addition, active ABMT (Hedges g -0.17; P=.004) had significant effects on reducing attentional bias, while placebo ABMT did not significantly alter attentional bias (Hedges g -0.04; P=.66). Our understanding of smartphone-delivered ABMT's potential highlights the value of both active and placebo interventions in mental health care. The insights from the moderator analysis also showed that tailoring smartphone-delivered ABMT interventions to specific threat stimuli and considering exposure duration are crucial for optimizing their efficacy. This research underscores the need for personalized approaches in ABMT to effectively reduce attentional bias and symptoms of mental health problems. PROSPERO CRD42023460749; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=460749.

Trajectories and influencing factors of psychological resilience among Chinese patients with maintenance hemodialysis

ObjectiveTo identify distinct trajectories of psychological resilience among Chinese patients with maintenance hemodialysis, explore influencing factors and inform the formulation of corresponding interventions. MethodsThis was a multi-center longitudinal study with a 6-month follow-up. With convenience sampling, a total of 231 patients with maintenance hemodialysis were recruited between September 2020 and July 2021. Patients’ characteristics, including sociodemographic information, social support and family resilience was collected through structured questionnaires as potential baseline influencing factors of psychological resilience trajectories. Psychological resilience was evaluated using the 25-item Chinese version of the Conner and Davidson resilience scale. Latent class growth modeling was conducted to identify homogeneous subgroups with distinct trajectories of psychological resilience. Univariable and multinomial logistic regression analysis were used to examine whether baseline influencing factors were associated with trajectories in patients with maintenance hemodialysis. ResultsFive distinct psychological resilience trajectory groups were identified: declining group (n = 20, 8.7 %), rising group (n = 17, 7.4 %), moderate-stable group (n = 128, 55.4 %), high-stable group (n = 7, 3.0 %) and low-stable group (n = 59, 25.5 %). High-stable group and moderate-stable group were combined into the well-psychological resilience group for multinomial logistic regression analysis. The multinomial logistic regression analysis showed that influencing factors associated with trajectories of psychological resilience were age, religion, monthly household income per capita, and baseline family resilience. ConclusionsThe results highlight the heterogeneity in the development of psychological resilience among Chinese patients with maintenance hemodialysis. There is a need for healthcare professionals to screen for trajectories of psychological resilience in Chinese maintenance hemodialysis patients and prepare individual mental healthcare interventions.

Emoji Support Predictive Mental Health Assessment Using Machine Learning: Unveiling Personalized Intervention Avenues

Mental health disorders, including anxiety, depres-sion, and stress, profoundly impact individuals’ well-being and necessitate effective early detection for timely intervention. This research investigates the predictive capabilities of machine learning algorithms in assessing anxiety, depression, and stress levels based on questionnaire-derived scores. Utilizing a dataset comprising self-reported scores obtained through a tailored questionnaire designed for mental health assessment, we delve into the application of Decision Trees, Naive Bayes, Support Vector Machines (SVM), and Random Forests for prediction. Data preprocessing involved comprehensive cleaning, encoding categorical variables, and careful feature selection, ensuring the relevance of features in the predictive models. Each algorithm un-derwent individual implementation, wherein we scrutinized their performances in predicting mental health conditions. Evaluation metrics such as accuracy, precision, and recall were employed to assess the models’ proficiency in predicting anxiety, depression, and stress levels. The findings underscore the potential of machine learning in accurately predicting mental health conditions based on questionnaire responses, offering insights into personalized interventions and early detection systems. This study contributes to advancing the understanding of machine learning applications in mental health assessment, highlighting avenues for impactful interventions in mental health care.

“It Doesn’t Feel Easy at All”: Stressors and Supports of Parenting Sexual and Gender Minority Adolescents in the US Southeast

Parents/caregivers of sexual and gender minority (SGM) adolescents play a vital role in supporting their children, yet limited research considers the perspectives of these parents. We conducted semi-structured interviews and focus groups via Zoom with 16 parents of 17 SGM adolescents aged 13–17 residing in Alabama, Georgia, and Tennessee. Parents reported that their SGM adolescents were, on average, 15 years old, most were assigned female sex at birth, identified as transgender, non-binary, or gender-nonconforming, and had received mental healthcare treatment for serious mental health difficulties. A flexible thematic analysis approach identified themes structured around the social-ecological model. Parents described stressors at the structural level (e.g., ramifications of anti-SGM legislation), interpersonal level (e.g., breaking ties with unsupportive religious communities), and intrapersonal level (e.g., exhaustion with being forced into an advocacy position). Parents also reported several sources of support that cut across the three socio-ecological levels and helped them to overcome barriers, challenges, and stressors. This study underscores the difficulties confronted by parents of SGM adolescents in the US Southeast, particularly within an increasingly hostile socio-political climate. Findings highlight the necessity for resources tailored to parents of SGM adolescents to help them navigate multi-level stressors.

African Accounts of Religious Conversations and Interventions in Mental Healthcare

The importance of healthcare has attracted conversations from healthcare professionals, as well as other groups like the United Nations, academic institutions, governments, the private sector, and religious organisations, all contributing to healthcare conversations because healthcare is foundational to human survival. However, there are questions on the quality and gaps of conversations and interventions regarding mental health among religious groups. This is because the quality of healthcare delivery may be rooted in the quality of conversations and interventions. This study explores the quality of mental healthcare (MHC) conversations and interventions in Traditional Religions, Christianity, and Islam in Africa. It interrogates past and recent conversations and interventions within the frameworks of spiritual care (SC) as a form of compassion science, interreligious collaborations, spiritual healings, and allopathic collaborations. Lastly, it places a searchlight on the loopholes of spiritual care in MHC and recommends closure where possible in the pursuit of improved healthcare and well-being in Africa.

Introduction to the Special Issue on the 39th Annual Meeting of the International Society for Traumatic Stress Studies: Scalable strategies to address the impact of trauma worldwide.

This editorial summary provides an overview of the 39th annual meeting of the International Society for Traumatic Stress Studies (ISTSS) held from November 1-4, 2023. The meeting, themed "Scalable Strategies to Address the Impact of Trauma Worldwide: Innovations and Implementation," encouraged presenters to focus on the scalability of traumatic stress research and practice. The articles presented at the meeting, which form this issue, cover a broad spectrum of topics, from basic biological mechanisms to innovative digital engagement to population-wide treatments. These articles emphasize eliminating biases in research design and psychiatric practice, promoting health equity, and addressing the unique challenges of traumatic stress. This summary underscores the importance of scalability in developing flexible, dynamic, and inclusive mental health care interventions.

Prevalence of psychiatric comorbidities and cognitive dysfunction among chronic kidney disease patients in a general hospital.

Chronic kidney disease (CKD) presents a profound clinical challenge, not only affecting physical health but also significantly impacting mental well-being. The complex interplay between CKD and psychiatric morbidities remains understudied. To address the existing gap by investigating the prevalence and patterns of psychiatric morbidity and cognitive dysfunction among CKD patients. This study adopted a cross-sectional and hospital-based study design. It included 72 patients diagnosed with CKD who fulfilled the inclusion and exclusion criteria. Structured clinical interviews, validated assessment tools (GHQ-12, HAD-S, AUDIT, Addenbrooke's Cognitive Examination-III), and a proforma gathered data on socio-demographic factors, illness severity, treatment history, and psychiatric history. 70.1% of participants exhibited psychological distress, indicating a high prevalence of psychiatric morbidity. Anxiety disorders were prevalent, affecting 58.1% of participants, while depression affected 32.3% (12.9% mild, 20.8% moderate, 4.8% severe). Cognitive impairment varied with CKD severity, with Stage 3 showing better scores than Stage 4, and hemodialysis patients exhibiting the most significant decline. This study underscores the substantial burden of psychiatric morbidity and cognitive dysfunction in CKD patients within a general hospital setting. Early identification and integrated interventions in mental health care are crucial for improving CKD patient outcomes and well-being.

The anxiety care continuum and its association with viral suppression among persons with HIV.

It is unclear how often anxiety is diagnosed and treated and whether anxiety treatment is associated with improved viral suppression in persons with HIV. In this study, we characterized the anxiety care continuum and its association with viral suppression in a large urban HIV clinic in the United States. Observational cohort study. We described the anxiety care continuum by combining data on self-reported anxiety symptoms, engagement in mental health care, clinical diagnoses and prescriptions from 1,967 persons receiving HIV care and treatment in Baltimore, Maryland, from 2014-23. We examined cross-sectional associations with viral suppression. All analyses were stratified by sex and race/ethnicity; a secondary analysis adjusted for age, years in care, and depressive symptoms. Nearly 1 in 5 patients reported mild-severe symptoms of anxiety but were not currently receiving mental health care or pharmacologic treatment for anxiety; 6% of patients reported anxiety symptoms but were receiving treatment, and 7% had been treated for anxiety that was currently in remission. The prevalence of viral suppression ranged from 87-89% across the anxiety care continuum except among patients with untreated moderate-severe anxiety, only 81% of whom were virally suppressed (95% CI: 80, 83). In adjusted models, untreated moderate-severe anxiety remained associated with viral non-suppression across demographic groups. We observed a robust association between untreated anxiety and viral non-suppression in a large urban cohort of persons with HIV. Screening for anxiety may identify patients with unmet mental health care needs who face barriers to maintaining viral suppression.

Sexual Orientation Differences in Age of First Treatment for a Mental Health Diagnosis: A Population-Based Study of Childhood and Adolescence

The aim of the current study was to identify the age at which sexual orientation disparity in mental health diagnoses can be first identified, as well as gender and sexual identity subgroup differences in such treatment, in a population-based sample. Young people aged 16 to 25 (N= 10,365) participating in the probability-based Swedish National Public Health Survey in 2018 were included. This sample was linked to physician-assessed mental health care treatment history data starting when all participants were 8 years old using national health care registries. Participants reporting a sexual minority identity in adolescence and young adulthood were more than 3 times as likely to have been treated for an internalizing disorder diagnosis (eg, depression, anxiety) and more than twice as likely to have been treated for a neurodevelopmental disorder diagnosis during childhood than participants reporting a heterosexual identity. Sexual minority participants overall and women in particular were more likely to have been treated for an internalizing disorder diagnosis at an early age compared with heterosexual participants, with this disparity starting at age 13. The sexual orientation disparity in likelihood of treatment for a neurodevelopmental disorder diagnosis was particularly elevated among bisexual/pansexual women with this disparity starting in early/middle adolescence. This population-based study linked to physician-assessed mental health diagnoses during childhood and adolescence identifies the age at which sexual orientation differences in treatment for common mental disorders emerge. The early emergence of this disparity suggests a potential benefit of interventions that facilitate social belonging for all youth.

Psychoeducation group facilitation training for nurses: A quality improvement project.

According to the 2020-2021 National Survey of Children's Health, 23.3% of children and adolescents met criteria for one or more mental, emotional, developmental, or behavioral problems. However, the prevalence of depression, anxiety, and post traumatic stress disorder have risen dramatically since the COVID-19 pandemic, leading to an increase in suicidal ideations, completed suicides, and pediatric psychiatric hospitalizations. There is a critical need for high quality mental healthcare treatments, including psychoeducation. Inpatient psychiatric nurses are well positioned to lead psychoeducation groups, but few receive training. Nurses at a designated inpatient pediatric psychiatric unit expressed discomfort in facilitating psychoeducation groups. This is a one-group prepost and follow-up quality improvement study designed to improve the quality of psychoeducation group facilitation skills provided by nurses at a child and adolescent inpatient psychiatric unit. A convenience sample of nurses (N = 16) participated. Baseline (T1), post-test (T2), and follow-up (T3) data were collected regarding nurses' self-efficacy, evidence based-practice attitudes, and knowledge of psychoeducation group facilitation. Nurses received 45 min of psychoeducation group facilitation education and 15 min of skills simulation. The nurses' knowledge, self-efficacy, and attitudes toward evidence-based practice when facilitating psychoeducation groups increased significantly from the baseline to the post-test, which was sustained from the post-test to the follow-up period. Study findings indicate the importance of supporting nurses in their role of psychoeducation group facilitation through education and program structure. Further research is needed to assess longer term sustainability and efficacy in leading psychoeducation groups.