Mental health conditions account for 18% of years lived with disability worldwide. 1-in-6 adults are affected in England, with most mental health conditions beginning in childhood and adolescence. Mental distress and ill health are unequally distributed in the UK, with strong associations with wider determinants of health, and higher prevalence among systemically disadvantaged groups. Currently, there is a lack of evidence to inform effective and timely policymaking for primary prevention in the UK. In recognition of these challenges, a national Population Mental Health (PMH) Consortium was established, as part of Population Health Improvement UK (PHIUK). PHIUK is a national research network which works to transform health and reduce inequalities through change at the population level. Our aim is to establish an interdisciplinary PMH Consortium, focussing on upstream determinants and the prevention of risks and onset of mental health conditions through interdisciplinary stakeholder engagement, to create new opportunities for population-based improvement of mental health in the UK.The PMH Consortium brings together leading interdisciplinary representation in population mental health, spanning from sciences to the arts, across the UK. Membership includes six academic institutions, third sector organisations, lived experience expertise, and strong links with national bodies to ensure integrated cross-national and regional policy impact. The PMH Consortium comprises four cross-cutting platforms (Partners in policy, implementation, and lived experience; Data, linkages, and causal inference; Narrowing inequalities; Training and capacity building) and three challenge areas (Children and young people's mental health; Prevention of suicide and self-harm; Multiple long-term conditions) which are highly integrated and interdependent. The work will be underpinned by a Theory of Change across an initial four-year life cycle. This paper describes the aim, objectives, and approach of the PMH Consortium, as well as anticipated challenges and strengths. The goal of the PMH Consortium is to develop a model for population mental health research and policy translation that is both scalable and sustainable. It is critical to ensure continued impact and viability beyond the initial four years, contributing to the prevention of mental health conditions in the UK, with personal, economic, social, and health benefits.
Headaches have been consistently associated with mental health disorders. However, current prevalence of headaches in Arab populations is highly varied. Additionally, the potential role of religiosity in this relationship has not been studied. This study aimed to estimate the prevalence of headaches in Saudi Arabia, a highly religious Arab society, to examine their association with mental health disorders, and to explore whether religiosity modifies this relationship. This study used the Saudi National Mental Health Survey (SNMHS), a nationally representative, cross-sectional, community-based psychiatric epidemiological household survey. Trained interviewers assessed history of headaches, and common DSM-IV mental health disorders were diagnosed using the Composite International Diagnostic Interview (CIDI). Religiosity was measured using a validated culturally appropriate religiosity scale embedded within the survey. Headache prevalence was calculated as the proportion of respondents reporting headaches relative to the total sample. Survey-weighted logistic regression models were used to estimate adjusted association between headaches and mental health disorders. An interaction term was introduced to explore the role of religiosity. The overall lifetime prevalence of headaches among Saudis was 56% (95% CI: 53%-58%), with 28% reporting recent episode of headaches. In multivariable logistic regression models including sociodemographic factors, respondents with headaches were more likely to have mental health disorders (OR: 2.09, 95% CI: 1.50-2.91; p < 0.001). Religiosity did not modify the association between headaches and mental health disorders (interaction OR: 1.00, 95% CI: 0.98-1.02; p > 0.9). Headaches are highly prevalent in Saudi Arabia, with more than half of respondents reporting a lifetime history and nearly one-third experiencing recent episodes. Individuals with headaches are more likely to have mental health disorders. However, varying levels of religiosity do not appear to modify this relationship. These findings underscore the importance of screening for mental health disorders in individuals presenting with headaches, regardless of their religiosity, and highlight the potential value of collaborative models that integrate professional mental health support with religiously sensitive approaches.
Incarcerated populations face greater health challenges, including higher rates of communicable and mental diseases. However, traditional health measures like disease prevalence and life expectancy do not capture their physical, mental, emotional, and social well-being. This scoping review will summarize the health-related quality of life (HRQoL) outcomes in incarcerated populations using preference-based HRQoL instruments (and measures that can be used to derive utility scores), providing insights for health policies and economic evaluations. A scoping review was conducted following PRISMA-ScR guidelines. Six electronic databases and three health technology assessment agencies were searched for peer-reviewed studies reporting preference-based HRQoL or HRQoL scores that can be used to generate health state utility values in incarcerated populations. Eligibility and data extraction were performed by two independent researchers.Findings were synthesized to identify knowledge gaps. Twenty-two articles met the inclusion criteria, primarily focusing on male and white populations. Ten studies targeted disease-specific populations, with mental health disorders (n = 7) being the most prevalent. Across studies, inmates generally reported lower HRQoL scores than the general population, especially those with mental health issues. Female and Indigenous inmates had lower HRQoL scores than male and non-Indigenous inmates. The variety in HRQoL instruments used, with each assessing different domains, hinders direct comparisons between studies. Validating instruments specific to incarcerated populations may be needed for future research. Overall, incarcerated populations, especially women and Indigenous inmates, demonstrate poorer HRQoL than the general population. There is a need for more diverse, inclusive studies to address these gaps. Incarcerated populations face greater health issues that are not fully captured by traditional health measures. Health-related quality of life (HRQoL) provides a more comprehensive view of their physical, mental, emotional and social well-being. This study summarizes HRQoL research in incarcerated populations using standardized tools. The purpose of this study is to provide a scoping review of the HRQoL outcomes of incarcerated populations, summarizing existing research and identifying gaps in the literature. Our findings reveal that inmates generally have lower HRQoL scores compared to the general population, and those with mental health issues reporting the lowest scores. Additionally, female and Indigenous inmates tend to have poorer HRQoL than male and non-Indigenous inmates. The findings highlight the need for HRQoL tools specifically tailored to incarcerated populations and call for more diverse studies, particularly for underrepresented groups.
Racial and ethnic inequities persist in medication treatment initiation and adherence for pregnant and postpartum people with opioid use disorder (OUD). Our objective was to understand the experiences of "positive outliers," specifically pregnant and postpartum people of color with OUD who utilized medication treatment and engaged in a randomized clinical trial for buprenorphine despite historical, cultural, and structural barriers. We conducted two sets of semi-structured qualitative interviews. First, trained peers with lived expertise as mothers in recovery interviewed individuals who identified with a non-white race and/or ethnicity and enrolled in the Medication Treatment for OUD in Expectant Mothers (MOMs) trial (NCT03918850). Second, we interviewed principal investigators, clinicians, and research coordinators from the 13 MOMs trial sites. We used an inductive thematic approach informed by the Social Ecological Model of Racism and Anti-Racism. Transcripts were double-coded and reviewed until consensus was reached. Preliminary findings from participant and staff interviews were merged and triangulated with peers to inform theme development. We completed 17 interviews with MOMs trial participants from 7 sites. Participants identified as Hispanic (29%), Black non-Hispanic (24%), multi-racial Hispanic (18%), multi-racial non-Hispanic (18%), and American Indian, Native Hawaiian, or Pacific Islander (12%). Thirty-two interviews with trial staff were also completed. Three themes emerged: (1) Although some participants expected racist treatment and research exploitation, all participants interviewed reported non-discriminatory, non-judgmental care within the MOMs trial; (2) Compassionate care, frequent, personalized, and integrated encounters, and emotional support helped counteract prior stigmatizing and discriminatory health care interactions, enabling participants of color to feel particularly supported, trusted, and empowered during the MOMs trial; and (3) Despite pervasive cultural stigma around addiction and concerns about taking an investigational drug while pregnant, participants expressed that pregnancy status, care team trust, and transparent communication with MOMs trial staff encouraged medication utilization and adherence. Facilitators of successful engagement in the MOMs trial and retention in medication treatment among pregnant and postpartum people of color with OUD included non-judgmental care, sustained trust, and frequent contact. Key perinatal OUD clinical interventions and trial improvements include personalized communication and scheduling flexibility to promote engagement of marginalized populations.
Given theoretical and methodological criticisms surrounding coping strategies, this study examines coping profiles and differences between mothers and fathers at the time of their child's autism diagnosis. Multi-group confirmatory factor analyses (MG-CFAs) were conducted to improve construct validity of the French Ways of Coping Checklist-Revised in 554 parents in France and to test measurement invariance between mothers and fathers. Linear mixed models were performed to examine parental status (mother vs. father) differences in coping strategies. Dyadic latent profile analysis (LPA) was used to identify distinct coping profiles and the R3STEP approach to examine differences in latent profile membership by parental status. MG-CFAs supported four coping dimensions (problem solving-positive reappraisal, seeking social support, wishful thinking, and self-blame) and demonstrated configural and metric invariances, with partial scalar invariance between mothers and fathers. Fathers reported a significantly lower use of all coping strategies except wishful thinking. LPA identified three coping profiles -Varied Coping, Adaptive-Dominant Coping, and Maladaptive-Dominant Coping-with no significant differences in latent profile membership between mothers and fathers. In both parents, coping profiles differed by anxiety symptoms; additionally, maternal profiles were associated with socio-economic status, stress levels, and the child's internalizing difficulties, and paternal profiles with depressive symptoms. These findings provide a more nuanced understanding of mother-father differences in coping among parents of autistic children and underscore the need for tailored, profile-based interventions in clinical practice and future research.
Borderline personality disorder (BPD) is highly stigmatized. Stigma, including clinicians' resistance, stigmatizing attitudes, and discriminatory beliefs, could be mitigated by a better knowledge of the disorder. This study evaluates the impact of a one-day training session on stigmatization by health personnel (HP). This two-center study prospectively included 172 HP who completed a face-to-face interactive training day embodying dialectical and destigmatizing positions. Elements of psychoeducation, emotional dysregulation model and practical tools were presented. Stigma attitudes and open-mindedness were assessed by the Opening Minds Stigma Scale for Health Care Providers self-questionnaire (OMS-HC); and beliefs (feeling of incompetence, pejorative perception of prognosis, guilt) by a custom Beliefs Questionnaire (BQ). Scores before and immediately after the training were compared using Student's paired t-test. Most HP worked in psychiatry (69%) and had no prior education on BPD (89%). Nurses were most represented (35%), ahead of nursing assistants (22%), psychologists (18%), and psychiatrists (10%). All scores decreased after training (p < 0.001): total OMS-HC (MD ± SD=-4 ± 8), attitude sub-score (2 ± 4), disclosure sub-score (1 ± 4); total BQ (6 ± 9), nurse feeling of incompetence sub-score (4 ± 4) and pejorative perception of prognosis sub-score (-2 ± 3). A one-day training session reduces HPs' stigmatizing attitudes and beliefs and has a positive impact on knowledge and open-mindedness about BPD patients. Training can lean on education about BPD nature, treatment and prognosis, experience-sharing with practical cases, and testimonies. It would enable compassionate and destigmatizing care. Further research is needed about the clinical impact of BPD training and its wider implementation in mental healthcare settings.
Open Dialogue has been linked to better outcomes and reduced hospital admissions amongst patients with mental health problems. Yet, information on associated health care costs is scarce. To conduct an evaluation of downstream health care costs of Open Dialogue provided to young patients in acute psychiatric crises and compared with treatment as usual. Matched case-cohort study based on clinical and register data. Open Dialogue was offered between 2000 and 2019 as standard care to adolescents in acute psychiatric crisis in four municipalities in Region Southern Denmark. 355 individuals between 14 and 19 years received treatment with Open Dialogue and were compared to 979 peers who had received standard acute psychiatric treatment in two other Danish Regions (Central Denmark Region and North Denmark Region) where Open Dialogue was not implemented. Health care cost data (including primary care, psychiatric and somatic care) was available during 2005-2018. We matched controls to the cases based on a X-factor propensity score and a 3:1 ratio. The statistical analysis took a double-robust approach combining matching with Difference-in-Difference analysis over 12-year follow-up. Graphical inspection and placebo tests were used to test parallel trends assumption, and generalized estimation equations were applied as a robustness check to validate the results. In the intervention group, the unadjusted yearly mean health care costs were €299 the year before receiving Open Dialogue. In the subsequent year, it was €1523, equivalent of a €1224 increase. In corresponding years, the respective health care costs were €208 and €1813 for members of the control group, implying an increase of €1605. The increase in health care costs was driven by psychiatric costs in both groups. Follow-up up to 12 years showed a decrease in total health care costs to €457 in the Open Dialogue group and €938 in the control group. The difference between the groups was not statistically significant. This evaluation did not find statistically significant differences in total health care costs between patients receiving Open Dialogue and controls over 12-year follow-up. Young patients in treatment with Open Dialogue during acute psychiatric crisis did not have higher total health care costs up to 12-year follow-up compared to controls.
Health care workers (HCWs) are particularly vulnerable to mental health challenges during public health crises such as pandemics. Subthreshold post-traumatic stress disorder (PTSD) is prevalent among frontline HCWs and may progress to full-blown PTSD without timely intervention. This study aimed to evaluate the safety, feasibility, and effectiveness of a newly developed virtual reality (VR)-based stabilization (VRS) program for frontline HCWs with above-subthreshold PTSD. A randomized controlled trial compared VRS with a mobile application-based stabilization (MAS) program, both using identical stabilization techniques. The trial was conducted at a COVID-19-designated hospital in South Korea. Thirty-six HCWs with above-subthreshold PTSD completed a 5-week stabilization program, with 18 participants in the VRS group and 18 in the MAS group. Primary outcomes were post-traumatic stress symptoms (PTSS), assessed via self-report (PTSD Checklist-5) and clinician rating (Clinician-Administered PTSD Scale for DSM-5). Secondary outcomes included depressive symptoms (Beck Depression Inventory-II, Hamilton Depression Rating Scale-17), anxiety (State-Trait Anxiety Inventory-Y), and quality of life (the World Health Organization Quality of Life Scale-Abbreviated Version). Concentration, immersion, task load, and cybersickness were also measured. Both interventions demonstrated satisfactory safety and feasibility. Immersion was significantly higher with VRS. Significant pre-post improvements in PTSS, self-reported depression, and anxiety were observed in both groups. However, the VRS group showed greater improvements in quality of life. These results suggest the potential of immersive VR-based stabilization interventions as a useful tool for protecting the mental health of vulnerable populations, including HCWs, in public health emergencies during and beyond pandemics, when the traditional face-to-face delivery of intervention is limited.
Research using the multidimensional sleep health (MDSH) framework has increased globally, often relying on self-report measures. The Ru-SATED scale and Sleep Health Index (SHI) are common self-report measures of MDSH, but comparative data on their measurement properties and contextual characteristics remain limited. Seven electronic databases were searched for measurement properties and uses of the two scales over the past twelve years. This review identified 19 psychometric validation studies concerning two original and 17 cross-cultural, and summarized contextual comparison of MDSH measures and frameworks. Measurement properties of both measures were assessed with the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guideline, and contextual comparisons were conducted narratively. Both measures exhibited acceptable psychometric properties across diverse cultural settings, with the SHI findings showing greater consistency than those of the Ru-SATED scale. Aggregating the Ru-SATED and SHI frameworks fully covered the sleep characteristics assessed by five instruments grounded in the World Sleep Society initiative, encompassing regularity, satisfaction, alertness, timing, efficiency, duration, and disorder. Notably, the SHI framework incorporates targeted sleep disorder assessment while the Ru-SATED framework specifically excludes such assessment, highlighting the distinct focus and scope of each tool. Instrument selection depends primarily on research purpose, study sample, and intended use. We recommend characterizing both sleep health and sleep disorders to fully capture the complex relationships between sleep and health outcomes.
Wearable technology holds promise for improving mental health care by enabling continuous, objective monitoring of physiologic parameters. Building on decades of psychophysiology research, wearables can provide an additional source of measurement for implementing measurement-based care in learning mental health systems. This review describes wearable use across inpatient and outpatient settings, identifying gaps and opportunities in clinical care and research. While widely studied in outpatient, wearables hold immense potential for in inpatient settings. Advancements needed include user-centered design, better understanding of complex populations and settings, and use of modern analytical methods to generate clinically actionable mental health insights for all.
Digital phenotyping-the moment-by-moment quantification of human behavior using data from smartphones and wearables-offers new pathways for mental health research and care. This review summarizes current trends, tools, and applications of digital phenotyping, highlighting its growing clinical relevance in early detection, symptom monitoring, and personalized interventions. Although studies increasingly demonstrate its feasibility and clinical utility across conditions such as depression, anxiety, and schizophrenia, challenges persist. These challenges include inconsistent data quality, small and nonrepresentative samples, lack of methodological standardization, and pressing ethical considerations about privacy and transparency.
The integration of machine learning (ML) approaches with immune biomarker research may facilitate the identification of candidate markers for achieving personalized medicine approaches in severe mental illnesses (SMI). This systematic review synthesizes the available evidence on ML algorithms applied to immune biomarkers in major depressive (MDD), bipolar (BD) and schizophrenic spectrum disorders (SZ), examining their performance across different clinical uses including diagnostic, prediction, monitoring, prognostic categories, in accordance with the Food and Drug Administration - Biomarker, EndpointS, and other Tools (FDA BEST) framework. We performed a PRISMA-compliant systematic search of PubMed, Web of Science, Scopus and PsycINFO databases until 14 July 2025, including 43 eligible studies with a total sample of 11,556 participants, 8339 with SMI (3228 MDD, 2614 BD and 2497 SZs) and 3217 healthy controls. We systematically described population, ML input data (including blood collection conditions, pre-processing steps, sample type, laboratory assay, missing data, and multimodality), and algorithms (supervised versus unsupervised models, feature selection, validation strategy, outcomes, and performance metrics). Overall, ML models showed moderate to high but heterogeneous performance. Diagnostic applications were the most common (AUC = 0.650-0.990), though predictive, monitoring, and prognostic uses were underrepresented and more variable. Across disorders, pro-inflammatory markers (IL-6, IL-8, TNF-α, IFN-γ, CRP) and IL-10 emerged most consistently, and data-driven approaches suggested shared immune subtypes beyond categorical diagnoses. However, substantial methodological and biological heterogeneity was observed, including inconsistent handling of missing data, limited external validation, and variable feature selection. Immunology-specific sources of variability (such as fasting status, circadian rhythms, and measurement batch effects) were rarely addressed, and the long-term stability of immune-based ML signatures remains largely unexplored. These gaps currently limit clinical translation and underscore the need for standardized protocols and more rigorous ML pipelines.
Depression is a pervasive global mental health issue, yet access to trained professionals remains severely limited. With the rapid advancement of artificial intelligence (AI), digital tools are increasingly seen as a viable way to address this shortage. However, questions remain about how digital platforms for mental health care can be effectively designed. This study aimed to investigate, from an end user's (patient's) perspective, the potential use scenarios, desired features, and perceived risks of AI psychotherapists in depression treatment, providing design guidelines for their development. A grounded theory approach was applied to analyze qualitative responses from 452 individuals recruited via Amazon Mechanical Turk. Data were collected through a scenario-based online survey on AI-assisted depression treatment administered between March 2023 and May 2023. Participants responded to 3 open-ended questions regarding the potential use of AI in treating depression, the characteristics expected from an AI psychotherapist, and the associated perceived risks, along with demographic, control, and contextual measures. The open-ended responses were inductively coded into themes, with intercoder reliability established (Cohen κ=0.80). In addition, variations in themes were further examined across participant profiles, including social stigma, current depression severity, trust in an AI psychotherapist, and privacy awareness. Participants envisioned AI psychotherapists across 5 primary scenarios: diagnosis, treatment, consultation, self-management, and companionship. Key desired features include professionalism, warmth, precision care, empathy, remote services, active listener, personalization, flexible treatment options, patience, trustworthiness, and basic treatment alternative, while critical concerns include diagnostic inaccuracy, treatment errors, privacy breach, lack of human interaction, technical malfunctions, and lack of emotional engagement. Based on these findings, a general MoSCoW (must have, should have, could have, and won't have) prioritization framework was proposed to serve as a conceptual starting point for future AI system design and empirical validation in mental health care. Notably, feature prioritization varied across user profiles: individuals with higher stigma placed greater emphasis on privacy protection, those with more severe depression prioritized precision care and timely access, low-trust users de-emphasized remote services, and privacy-sensitive individuals showed reduced preference for features requiring extensive data disclosure. These patterns highlight the need for context-sensitive design. This study provides a patient-centered framework for designing AI psychotherapists and complements the existing literature by highlighting the importance of balancing clinical effectiveness with relational considerations. The findings offer actionable guidelines for designing AI mental health care tools that are aligned with user expectations and sensitive to individual differences.
ObjectivesThis study aimed to compare serum leucine-rich repeat and fibronectin type III domain-containing protein-5 (LRFN5) and olfactomedin-4 (OLFM4) levels and aggregate index of systemic inflammation (AISI) values between hospitalized subjects with bipolar disorder (BD) experiencing acute manic episodes with psychotic features and healthy controls (HCs), and to examine their associations with clinical features and symptom severity.MethodsIn this cross-sectional study, participant characteristics and clinical features were assessed by structured clinical interviews. LRFN5 and OLFM4 levels were measured in the BD (n = 37) and HC (n = 35) groups using Enzyme-Linked ImmunoSorbent Assay kit.ResultsSerum LRFN5 (adjusted P = 0.0117) and OLFM4 (adjusted P = 0.0117) levels were significantly lower, whereas AISI (adjusted P = 0.0005) levels were significantly higher in the BD group compared with HCs, after adjusting for age, gender, body mass index, and smoking status. Within the BD group, a strong positive correlation was observed between LRFN5 and OLFM4 levels (r = 0.702, adjusted P = 0.006) and AISI showed a significant positive correlation with manic symptom severity score after controlling for age, gender, body mass index, and smoking status (r = 0.472, adjusted P = 0.030). In binary logistic regression analysis adjusted for age, gender, body mass index, and smoking status, lower OLFM4 levels (odds ratio (OR) = 0.970, P = 0.020, adjusted P = 0.003) and higher AISI values (OR = 1.008, P = 0.002, adjusted P = 0.001) were independently associated with BD status, alongside smoking status (OR = 19.213, P = 0.005, adjusted P = 0.001) (apparent area under the curve (AUC) = 0.914, optimism-corrected AUC = 0.884). After repeated stratified holdout validation, the mean and median test AUCs were 0.868 and 0.876, respectively.ConclusionsSubjects with BD experiencing acute manic episodes with psychotic features exhibited decreased circulating LRFN5 and OLFM4 levels alongside an increased systemic inflammatory burden, as reflected by AISI. AISI showed the strongest association with BD status and symptom severity and OLFM4 remained significant in adjusted analyses. Rather than indicating definitive diagnostic utility, the observed alterations may instead reflect underlying biological processes related to BD.
This article presents a review of examples of digital mental health technology (DMHT) for assessing and treating posttraumatic stress disorder (PTSD), including research supporting these innovative solutions. Tools for assessing PTSD are reviewed, including digital administration of self-report measures, ecological momentary assessment methods, personal sensing, electronic medical record and other naturalistic data sources, and emerging digital assessment tools. Next, DMHTs for PTSD treatment are reviewed, including Internet-based interventions, mobile mental health apps, virtual reality therapy, and several emerging digital interventions. DMHT applications for PTSD have demonstrated promise in research and are beginning to be used in clinical practice.
To investigate the current status and dyadic mechanism of maternal and spousal role adaptation for postpartum anxiety and depression. Convenience sampling was employed to select 276 pairs of mothers and their spouses from October 2023 to October 2024 as study participants. Binary analyses were conducted using the actor-partner interdependence model (APIM) and the mediation model. The prevalence rates of anxiety and depression during the first postpartum year were 38.5% and 14.5% among mothers, and 36.9% and 12.9% among their spouses, respectively. The APIM revealed that maternal and spousal role adaptation significantly and negatively predicted their own postpartum anxiety (maternal actor effect: β = -0.120; spousal actor effect: β = -0.081) and depression (maternal actor effect: β = -0.165; spousal actor effect: β = -0.113), and that role adaptation similarly predicted each other's anxiety (maternal partner effect: β = -0.115; spousal partner effect: β = -0.059) and depression (maternal partner effect: β = -0.129, spousal partner effect: β = -0.064). Mediation analyses suggest that future time insight and parenting competence mediate this process. Postpartum anxiety and depression serve as a dyadic phenomenon, where maternal and spousal role adaptation significantly affects mental health outcomes. Future time insights and feelings of parenting competence serve as mediating variables in the relationship. Not applicable.
Depression and anxiety are among the most common comorbidities in individuals with Parkinson's disease (PD). Yet their prevalence and contributing factors in Saudi Arabia are poorly examined. This study aimed to estimate the prevalence of depression and anxiety among patients with PD in Saudi Arabia, along with contributors, demographics, and clinical correlates. A cross-sectional study was conducted in Riyadh, Saudi Arabia, in which 130 patients diagnosed with PD completed the validated Arabic versions of the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) to ascertain the presence of depression and anxiety, respectively. Sociodemographic, patient-related factors, and clinical data were also collected and analyzed in conjunction with the aforementioned scales using univariate and multivariable logistic regression. Depression and anxiety were present in 67.7% and 62.3% of participants, respectively. Compared with the age group 41-50 years, the 51-60 and 61-70 groups were less likely to be depressed or anxious (P = .013 and P = .008). Lower education (high school or less) was associated with both depression and anxiety (P = .021 and P = .017). Anxiety (GAD-7) was associated with a history of major depressive disorder (P = .003). Longer PD duration was associated with higher odds of both anxiety and depression (P ≤ .001). Our study shows a high prevalence of anxiety and depression in patients with PD, particularly among those with longer disease duration and lower educational attainment. Routine neuropsychological screening and early multidisciplinary care are warranted.
A number of articles have heralded the use of artificial intelligence (AI) agents to serve as a replacement for human psychotherapists. Despite the rapid advancements in the use of both rule-based and generative AI programs in the recent past, an overall review shows only small impacts on certain mental health symptoms, particularly depression, and then only in the short-term. Significant strides forward, both in terms of technology and the development of answers to ethical questions regarding AI's use in psychotherapy, must be seen before the use of such systems becomes widespread or regularly recommended to replace human mental health clinicians.
Stigma is a critical barrier to adolescents' willingness to seek professional mental health support, yet less is known about whether non-stigmatizing attitudes toward peers with mental health problems are associated with adolescents' help-seeking attitudes. This study examined correlates of adolescents' attitudes toward seeking professional psychological help. A sample of 686 Chinese adolescents (Mean age = 16.67 years, SD = 2.39 years; 55.2% were females) completed measures of depressive and anxiety symptoms, non-stigmatizing attitudes toward peers with mental health problems (perceived competence and social acceptance), and attitudes toward seeking professional psychological help. Hierarchical regression analyses indicated that female adolescents reported more positive help-seeking attitudes compared to males. Higher levels of depressive and anxiety symptoms were associated with less favorable help-seeking attitudes. Notably, perceived competence positively predicted help-seeking attitudes, whereas social acceptance showed a negative association. The final regression model accounted for 13.1% of the variance in help-seeking attitudes. These findings suggest that adolescents' help-seeking attitudes are shaped not only by emotional distress, but also by how psychological difficulties are understood and evaluated within the peer context. Interventions may benefit from addressing both emotional distress and peer-related stigma processes while fostering supportive environments that make professional help more acceptable and accessible to adolescents.
Burnout and clinical depression are often experienced by medical students in the United States, which impacts individual wellbeing as well as professionalism, empathy, and patient care. This study aimed to evaluate stress and wellbeing among first-year medical students at one accredited M.D. institution by administering the Medical Student Stress Scale (MSSS), a context-specific measure designed to capture multidimensional sources of medical student stress. The MSSS, a 22-item questionnaire, was administered to first-year medical students during the 2024-2025 academic calendar, in the fall and spring, alongside a Brief Resilience Scale (BRS) and demographic questionnaire. According to the MSSS, stress was measured by calculating a summative score, ranging from 0 to 88, with higher scores indicative of greater levels of stress. The BRS measures resilience with a total score determined as a summation of the six item responses, categorized as low (1-2.99), normal (3-4.3), or high (4.31-5). The overall response rate was 61% (107/175) in the fall and 47% (87/175) in the spring. Average student stress scores in the fall and spring were 34.3 and 38.8, respectively. The resilience score in the fall and spring was 3.6 and 3.5, respectively. Multivariable linear regression showed that student stress decreased by 10 and 13 points with every 1-point increase on the BRS in the fall and spring, respectively (p < 0.001). This correlates with a 11-15% reduction in stress. Additionally, at both time points, males displayed a significantly lower estimated stress score than females (p = 0.044 and p = 0.016). In the spring, compared to students of Christian faith, Jewish students displayed an estimated 10-point increase in stress (p = 0.024), and Muslim students displayed an estimated 17-point increase in stress (p = 0.005). Additionally, students that reported they were low-income displayed an estimated 8-point increase in stress compared to non-low-income students (p = 0.009). To identify trends in both stress and resilience, the MSSS and BRS are feasible surveys to implement in medical schools. Understanding how stress and resilience are affecting medical students provides an opportunity to create tangible interventions to better support student wellness and create resilient physicians.