The bivalent fear of evaluation model proposes that fear of negative evaluation (FNE) and fear of positive evaluation (FPE) are distinct but related constructs, and that social anxiety arises when they are elevated. This represents a variable-centered perspective. However, a recent review suggested that individuals may be affected by unique combinations of FNE and FPE because they have different functions, mechanisms, and outcomes. Such patterns can be revealed by taking a person-centered approach. To explore this, the current study employed latent profile and k-means clustering analyses in four independent samples (total n = 2,913) to identify subgroups based on FNE and FPE levels. Between-group levels of social anxiety were further examined. Results demonstrated that participants could be classified into four groups: (1) high bivalent fears of evaluation group, (2) high FNE group, (3) high FPE group, and (4) low fears of evaluation group. Importantly, significant differences in social anxiety levels were found among these groups. Our findings extend the bivalent fear of evaluation model to a person-centered perspective and enable clinicians to tailor interventions that specifically address distinct underlying fears of evaluation. This may facilitate more effective treatment for individuals with social anxiety.
Objective: Given the stressors experienced during the COVID-19 pandemic, it is critical to identify populations with elevated mental health needs during this crisis. This study investigated demographic correlates of reported intention to utilize mental health (MH) and suicide prevention (SP) resources in a community sample during the COVID-19 pandemic.
Methods: A sample of 1,978 adults in the United States completed an anonymous online survey between June 2020 and February 2021.
Results: Intent to utilize MH resources was associated with younger age, single marital status, female gender, and Hispanic vs. White race/ethnicity. Intent to utilize SP resources was associated with younger age, single marital status, and was greater among Black and Hispanic vs. White race/ethnicity. Lower education was associated with MH and SP utilizers in bivariate analysis. Indirect effects of Suicide Crisis Syndrome (SCS) symptoms were found on the association of age, gender, and marital status with MH utilization and of age, marital status, and education with SP Utilization.
Conclusions: Specific demographic populations demonstrate greater interest in mental health care during the COVID-19 pandemic. These help-seeking patterns can be explained in part by an elevated level of SCS symptoms, suggesting greater levels of distress were driving expressed intention to utilize service referrals.
Background: Glial cell line-derived neurotrophic factor (GDNF) has emerged as a potential biomarker for schizophrenia (SCZ). However, GDNF levels remain unclear in affected individuals compared to healthy controls. Therefore, we aimed to calculate a pooled estimate of GDNF levels in patients with SCZ in comparison with healthy controls.
Methods: A systematic search was performed in PubMed, Scopus, Web of Science, and Science Direct for published studies from the first date available up to 17 June 2024. Twelve studies (n = 817 patients and 691 healthy controls) were included in the meta-analysis. Subgroup analyses and meta-regression were performed, addressing heterogeneity and publication bias.
Results: Random-effects estimates (d = -0.80, p < 0.001) of the present meta-analysis revealed a significant mean difference in GDNF levels between SCZ patients and healthy controls. Subgroup analyses indicated that the standardized mean difference of GDNF was larger in European samples (d = -1.01, p ≤ 0.001) than in the Asian population (d = -0.61, p = 0.011). Non-medicated SCZ patients (d = -1.08, p ≤ 0.001) exhibited lower GDNF levels than those on medication (d = - 0.70, p = 0.004). Additionally, patients with a disease duration of ≥ 10 years showed lower levels of GDNF (d = -0.93, p = 0.058 versus d = -0.82, p = 0.002).
Conclusions: The findings suggested that GDNF may be a promising biomarker and therapeutic target for schizophrenia. Future research should focus on elucidating the mechanisms underlying altered GDNF levels and exploring its implications for treatment strategies.
Background: Although impaired cognitive control is common during the acute detoxification phase of substance use disorders (SUD) and is considered a major cause of relapse, it remains unclear after prolonged methadone maintenance treatment (MMT). The aim of the present study was to elucidate cognitive control in individuals with heroin use disorder (HUD) after prolonged MMT and its association with previous relapse.
Methods: A total of 63 HUD subjects (41 subjects with previous relapse and 22 non-relapse subjects, mean MMT duration: 12.24 ± 2.92 years) and 31 healthy controls were enrolled in this study. Eye tracking tasks, prospective memory tasks, the Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A) and the Prospective and Retrospective Memory Questionnaire (PRMQ) were used to assess cognitive control.
Results: HUD individuals exhibited worse saccade error rate and executive dysfunction but showed no significant impairment in prospective memory. Additionally, the relapsers performed worse in terms of antisaccade amplitude and velocity at higher difficulty gradients (11° or 16°). Antisaccade performance in terms of amplitude and velocity was negatively correlated with executive function scores. Deficits in inhibition, cognitive flexibility, and self-monitoring were found to mediate the relationship between previous relapse and impaired antisaccade performance.
Conclusions: Even after prolonged MMT, HUD individuals still show partial impairments in cognitive control and antisaccade performance. Previous relapse exacerbates cognitive control deficits through executive dysfunction in inhibition, cognitive flexibility and self-monitoring, which can be screened by higher difficulty of antisaccade amplitude and velocity. More importantly, saccade error rate can reflect impaired inhibitory control in HUD individuals, whereas antisaccade amplitude and velocity appear to have potential diagnostic value for relapse.
Background: Individuals with GD may suffer from limited access to healthcare services because of negative attitudes from the healthcare providers. Therefore, to promote medical service it is of great importance to address negative attitudes among healthcare providers. The present study aimed to estimate the prevalence of transphobia as well as to assess the association between personality traits and transphobia among students of the University of Medical Sciences, who will hold key positions within the healthcare system.
Methods: This cross-sectional study was conducted on 418 students at Tabriz in northwestern Iran in 2024. Convenience sampling method was utilized. Data were gathered through standard self-reported questioners; Genderism and Transphobia Scale (GTS), and Big Five Inventory scale (BFI-44). Linear regression analysis was conducted to estimate standard coefficient with 95% Confidence Intervals (CIs).
Results: The response rate was calculated as 98.35% in which 204 (48.8%) out of the participants were female. The mean (SD) age was 23.73 (2.01) years. The adjusted multiple linear regression coefficients indicated that the agreeableness (β = 0.16, p = 0.001), neuroticism (β = 0.11, p = 0.02), and openness (β = -0.27, p = 0.001) were able to effectively explain the variance in transphobia scores. However, conscientiousness (β = 0.06, p = 0.18) and extroversion (β = 0.06, p = 0.05) did not significantly contribute to explaining the variance in transphobia scores.
Conclusions: Personality traits may significantly influence attitudes toward transgender individuals, suggesting that interventions designed to reduce transphobia should consider these foundational personality characteristics.
Clinical trial number: Not applicable.
Background: History of suicide attempts is one of the strongest predictors of adolescent suicide death. Our aim was to improve the comprehension of behavioral and socio-demographical characteristics of adolescent who have attempted suicide which can accelerate preventive and therapeutical measures.
Methods: We retrospectively analyzed medical data of 284 psychiatric inpatients aged 13-18. We performed an univariate and multivariate analyses for the whole group and female and male sex separately followed by a logistic regression analysis. The primary outcome measure was history of suicidal attempt (SA).
Results: 115 out of 284 analyzed patients (40.5%) - 91 girls (subgroup 1) and 24 boys (subgroup 2) - had a history of SA. In the whole group SA was associated with female gender, cigarette smoking, nonsuicidal self-injury (NSSI) and neglect of emotional or social needs. In the subgroup of girls the most significant association was found between SA and cigarette smoking, followed by neglect of emotional and social needs, NSSI and the older age of receiving psychiatric help. In boys, the history of SA was associated with two factors: cigarette smoking and family victimization.
Conclusions: The only factor that showed significant association with SA consequently throughout all our study was cigarette smoking, which implies that in the high risk adolescents population cigarette smoking might be a more specific characteristic of the history of SA than NSSI and thus should not be neglected during first examination.
The current DSM-oriented diagnostic paradigm has introduced the issue of heterogeneity, as it fails to account for the identification of the neurological processes underlying mental illnesses, which affects the precision of treatment. The Research Domain Criteria (RDoC) framework serves as a recognized approach to addressing this heterogeneity, and several assessment and translation techniques have been proposed. Among these methods, transforming RDoC scores from electronic medical records (EMR) using Natural Language Processing (NLP) has emerged as a suitable technique, demonstrating clinical effectiveness. Numerous studies have sought to use RDoC to understand the Diagnostic and Statistical Manual of Mental Disorders (DSM) categories from a qualified perspective, but few studies have examined the distribution variations and interaction characteristics of RDoC within various DSM categories through retrospective analyses. Therefore, we employed unsupervised learning to translate five domains of eRDoC scores derived from electronic medical records (EMR) of patients diagnosed with Major Depressive Disorder (MDD), Schizophrenia (SCZ), and Bipolar Disorder (BD) at West China Hospital between 2008 and 2021. The distribution characteristics, interaction networks, and potential clinical effectiveness of RDoC domains were analyzed. Using non-parametric statistical tests, we found that MDD had the highest score in Negative Valence System (NVS) (4.1, p < 0.001), while BD exhibited the highest score in Positive Valence System (PVS) score (4.9, p < 0.001) and Arousal System (AS) (4.4, p < 0.001). SCZ demonstrated the highest scores in Cognitive Systems (CS) (5.8, p < 0.001) and Social Processes Systems (SPS) (4.6, p < 0.001). Through Bayesian network (BN) analysis, we identified relatively consistent interaction relationships among various RDoC domains (NVS → AS, NVS → CS, NVS → PVS, as well as CS → SPS; parameter range = 0.156 to 0.635, p < 0.001). Lastly, using logistic regression and Cox proportional hazards models, we demonstrated that AS was significantly associated with the length of hospital stay (-0.21, p < 0.05) and 30-day readmission risk (adjusted odds ratio [aOR] = 0.91, 95% confidence interval [CI] 0.91-0.99) to some extent. In conclusion, we suggest that the eRDoC characteristics varied in different DSM. By Bayesian Network, we found NVS and CS might be potential source in interacting with other system. Furthermore, CS, SPS and AS were associated with the length of stay and 30-days readmission, making them effective for predicting prognosis of psychiatric disorders.
Background: Patients with obstructive sleep apnea (OSA) frequently experience sleep disturbance and psychological distress, such as depression and anxiety, which may have a negative impact on their health status and functional abilities. To gain a more comprehensive understanding of the symptoms of depression, anxiety, and sleep disturbance in patients with OSA, the current study utilized network analysis to examine the interconnections among these symptoms.
Methods: Depressive and anxiety symptoms were evaluated using the Hospital Anxiety and Depression Scale (HADS), and sleep disturbance symptoms were evaluated using the Pittsburgh Sleep Quality Index (PSQI). A total of 621 patients with OSA completed the questionnaires. The indices 'Expected influence' and 'Bridge expected influence' were used as centrality measures in the symptom network. The Least Absolute Shrinkage and Selection Operator (LASSO) technique and the Extended Bayesian Information Criterion (EBIC) were utilized to estimate the network structure of depressive, anxiety, and sleep disturbance symptoms. A Network Comparison Test (NCT) was performed to evaluate the differences between the mild to moderate OSA and severe OSA networks.
Results: Network analysis revealed that A6 ("Getting sudden feelings of panic") had the highest expected influence value and D6 ("Feeling being slowed down") had the highest bridge expected influence values in the networks. The NCT results revealed that the edge weights significantly differed between patients with mild to moderate OSA and those with severe OSA (M = 0.263, p = 0.008). There was no significant difference in global strength variation between the two networks (S = 0.185, p = 0.773).
Conclusions: Our results suggest that the highest expected influence value and bridge symptoms (e.g., A6 and D6) can be prioritized as potential targets for intervention and treatment in patients with OSA.
Background: To address the growing demand for psychological treatment, healthcare providers are increasingly utilising low-intensity interventions, characterised by reduced practitioner contact and emphasis on independent patient engagement with therapeutic materials through between-session work (BSW). While BSW is critical for maximising treatment outcomes, patients and practitioners report challenges with its completion. Research identifying factors influencing between-session engagement in Cognitive Behavioural Therapy (CBT) has largely focused on high-intensity CBT, limiting understanding within low-intensity contexts.
Methods: This study explored practitioner perspectives on the barriers and facilitators of BSW engagement within low-intensity CBT-based interventions. Using interpretive description methodology, 22 Psychological Well-being Practitioners (PWPs) from UK National Health Service (NHS) Talking Therapies services were interviewed. Inductive and deductive framework analysis was used to crossmatch data to an existing conceptual model of predictors for CBT BSW engagement.
Results: Practitioners identified patient-level barriers including passive treatment expectations, comorbid health conditions, social stressors, and reduced mental health literacy, particularly among ethnic minority populations. Practitioner-level facilitators involved clear task planning and personalised BSW tailored to patients' sociocultural environments. Organisational recommendations emphasised the need for diverse workforces and adequate training in culturally sensitive care.
Conclusions: Findings underscore the importance of practitioner behaviours in optimising patient engagement between-sessions, offering clear directions to enhance treatment outcomes in globally adopted low-intensity interventions.
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：