Psychiatric Quarterly最新文献

In this study, our aim was to develop a valid and reliable scale to determine the social anxiety level of people regarding COVID-19. A total of 420 people participated in the research. Explanatory and confirmatory factor analyses were used to test the validity and reliability of the scale. As a result of the explanatory factor analysis, it was determined that the scale consisted of 22 items and four sub-dimensions. These sub-dimensions were named "negative thinking", "sociability", "fear" and "vaccination". In examination of the fit indices obtained from the confirmatory factor analysis results, it was seen that the four-factor scale structure was high and acceptable. Based on these results, we concluded that the COVID-19 social anxiety scale (CAS) was valid and reliable for examining the level of social anxiety of people during the pandemic periods.

Recent research has shown that people who gamble are more likely to have suicidal thoughts and attempts compared to the general population. Despite the advancements made, no study to date has predicted suicide risk factors in people who gamble using machine learning algorithms. Therefore, current study aimed to identify the most critical predictors of suicidal ideation and suicidal attempts among people who gamble using a machine learning approach. An online survey conducted a cross-sectional analysis of 741 people who gamble (mean age: 25.9 ± 5.56). To predict the risk of suicide attempts and ideation, we employed a comprehensive set of 40 biological, psychological, social, and socio-demographic variables. The predictive models were developed using Logistic Regression, Random Forest (RF), robust eXtreme Gradient Boosting (XGBoost), and ensemble machine learning algorithms. Data analysis was performed using R-Studio software. Random Forest emerged as the top-performing algorithm for predicting suicidal ideation, with an impressive AUC of 0.934, sensitivity of 0.7514, specificity of 0.9885, PPV of 0.9473, and NPV of 0.9347. Across all models, dissociation, depression, and anxiety symptoms consistently emerged as crucial predictors of suicidal ideation. However, for suicide attempt prediction, all models exhibited weaker performance. XGBoost showed the best performance in this regard, with an AUC of 0.663, sensitivity of 0.78, specificity of 0.8990, PPV of 0.34, NPV of 0.984, and accuracy of 0.8918. Depressive symptoms and rumination severity were highlighted as the most important predictors of suicide attempts according to this model. These findings have important implications for clinical practice and public health interventions. Machine learning could help detect individuals prone to suicidal ideation and suicide attempts among people who gamble, assisting in creating tailored prevention programs to address future suicide risks more effectively.

Mental disorders are a global health issue affecting millions of individuals. People with mental disorders often face barriers to treatment, including stigma and discrimination, social, religious, and familial barriers, and a lack of knowledge about available treatment options. These barriers can lead to delayed or untreated mental illnesses, with serious consequences for the individuals, their families, and communities. This cross-sectional study explores the treatment barriers for different mental disorders as they significantly impact people's lives. It includes patients diagnosed with mental disorders. The data were collected from the patients or their companions attending Qassim Mental Health Hospital's outpatient clinic. We used a self-administered online questionnaire, sociodemographic variables, and the barrier to access to care evaluation (BACE) scale. Our sample included 332 adult patients with mental disorders (men, 63.9%; women, 36.1%). The mean percentage scores for stigma-, attitude-, and instrument-related barriers were 26.7%, 33.0%, and 18.7%, respectively. Younger age was associated with higher scores in all three BACE domains. Patients with psychotic disorders faced stigma- and attitude-related barriers, but not instrument-related barriers. Patients with multiple mental conditions faced increased stigma- and instrument-related barriers but not attitude-related barriers. Further longitudinal studies are required to better understand these barriers to help patients with mental conditions.

Researches indicate that children with dyslexia are more likely to experience psychiatric symptoms throughout life compared to peers in the general population. Improving the classification of psychiatric symptoms in this population can be a valuable goal for strengthing therapeutic models. The current study aimed to evaluate the dynamic associations between internalizing and externalizing symptoms in children with dyslexia using network analysis. This cross-sectional study was conducted in Saudi Arabia from April to July 2024, involving 183 children aged 8-11 diagnosed with dyslexia, recruited through convenience sampling from local schools and clinics. The Child Behavior Checklist (CBCL) was utilized to assess a range of psychopathological symptoms, focusing on six clusters: affective problems, anxiety problems, somatic symptoms, ADHD symptoms, oppositional defiant problems, and conduct problems. Notably, "cries a lot" emerged as a central symptom in the affective network, while "dependent" was identified as the most pivotal symptom in the anxiety network. Centrality measures-betweenness, closeness, and strength-were employed to quantify the significance of each symptom. The findings suggest that these central symptoms may contribute to the development and persistence of psychiatric comorbidities in children with dyslexia. Interventions targeting these core symptoms could enhance psychological outcomes for this population. Overall, this study lays the groundwork for future research into tailored intervention strategies to address unique psychiatric comorbidities associated with dyslexia.

Introduction: Fibromyalgia is a chronic condition often accompanied by cognitive impairments, such as difficulties with sustained attention, and emotional disturbances, particularly depression and anxiety. Understanding how these emotional and cognitive factors interact is crucial to improving treatment for fibromyalgia patients. This study aimed to investigate the network structure of these interactions, focusing on the relationship between depression, anxiety, and cognitive performance.

Method: A total of one hundred ten participants diagnosed with fibromyalgia completed self-reported assessments of depression and anxiety, alongside cognitive performance tests. Key measures included Continuous Performance Tasks (CPT) for impulsivity and sustained attention, the Stroop Interference Index (StI), Stroop Color Naming (StC), and Stroop Word Reading (StW). Network analysis was conducted to estimate the relationships between emotional and cognitive variables and assess their centrality within the network.

Results: Errors in CPT emerged as the most significant indicators of the relationship between depression and cognitive performance, with high centrality values observed for the Stroop tasks (StI, StC, and StW). Additionally, Trait Anxiety (A_T) and Beck Depression Inventory (BDI) measures showed high centrality, underscoring the critical role of emotional states in the network of cognitive and emotional variables. The centrality values for cognitive flexibility, processing speed, and inhibitory control were found to be 0.85, 0.78, and 0.72, respectively.

Conclusion: This study highlights the strong association between depression symptoms and deficits in cognitive domains, emphasizing the need for integrated care approaches that address both cognitive and emotional health. Multidisciplinary treatment programs, including cognitive rehabilitation and psychological support, could improve patient outcomes by targeting the most influential variables in the cognitive-emotional network.

This study aimed to evaluate the role of neuroinflammation in neuronal and glial cells in the pathophysiology of Major Depressive Disorder (MDD) through different biomarkers.S100-B and VILIP-1 levels of patients diagnosed with MDD were evaluated before and after antidepressant treatment. A total of 65 patients diagnosed with MDD and 69 healthy controls were included. Serum levels of S100B and VILIP-1 were measured at the time of diagnosis and after eight weeks antidepressant treatment and compared with healthy controls. Hamilton Depression Rating Scale (HDRS) and the Clinical Global Impression Scale (CGI) were applied to assess the severity of depression. In our study, although serum S100B levels were higher in patients before treatment compared to healthy controls, this difference was not statistically significant. Regarding VILIP-1 levels, there was no statistically significant difference between patients and healthy controls. A positive and statistically significant correlation was found between S100B and VILIP-1 levels in MDD group before the treatment. At the eighth week of treatment, a statistically significant positive correlation was also found between S100B and VILIP-1 levels. Our research is the first study to evaluate MDD through two separate biomarkers specific to glial and neuronal cells.The fact that S100B and VILIP-1 levels showed significant correlations in patients diagnosed with MDD both before and after treatment suggests that they may play a shared role in the pathophysiology of the disorder. The correlation between S100B and VILIP-1 may serve as a guide in understanding the pathophysiology of the disorder and in identifying new drug development targets.

The COVID-19 pandemic has significantly impacted mental health worldwide, leading to increased rates of suicidal behavior. This systematic review and meta-regression aim to investigate the global prevalence and risk factors associated with suicidal behaviors in the general population during the pandemic. The study included 202 articles from January 1, 2019, to October 31, 2023, sourced from databases such as Embase, MEDLINE, CINAHL, Web of Science, and Cochrane Library. The meta-analysis revealed a prevalence of 13.5% for suicidal ideation, 10.4% for suicide attempts, and a death rate of 0.5%, translating to 4.52 per 100,000 person-years. Significant risk factors identified include being transgender, young adults (18-44 years), unmarried status, low education, retirement, living alone, low social support, a history of suicide attempts, substance use, depression, anxiety, PTSD, sleep problems, poor perceived physical health, loneliness, quarantine, and residing in the Americas or multiple regions. The findings underscore the urgent need for targeted mental health interventions during pandemics, focusing on high-risk groups such as young adults, transgender individuals, those with low social support, and people with mental health issues. This comprehensive analysis provides valuable insights for policymakers and healthcare providers to develop effective strategies to mitigate the heightened risk of suicide during global health crises.

Patients with acute psychosis are often confronted with the prejudice that they refuse all treatment due to lack of insight. This study examined and compared the aims and preferences for treatment of patients with acute psychosis and of psychiatric inpatient staff. A total of 142 inpatients being treated for a psychotic disorder on either a locked or an open ward indicated which of a range of symptoms they want to be treated and which of various biopsychosocial treatment options they would like to receive. Staff members from the same wards reported which psychiatric symptoms they deemed relevant in the treatment of psychosis and which treatment options should be offered. Patients assigned the highest treatment need to neurocognitive symptoms, followed by affective and positive symptoms. In contrast, staff assigned the highest treatment need to positive symptoms, followed by neurocognitive and affective symptoms. Patients and staff on open wards expressed more treatment needs overall than did patients and staff on locked wards. Patients' desire for treatment differed across treatment types. In comparing patients on locked versus open wards, patients on open wards expressed higher approval of medication than patients on locked wards. Even patients with highly acute psychosis being treated on locked psychiatric wards endorsed treatment. Treatment preferences of this group deviated markedly in some instances from staffs' preferences. Considering their specific needs may contribute to increasing patient self-efficacy, and improving adherence to treatment.

Loneliness and depression are serious public mental health problems. Meaning in life (MIL) is associated with reduced loneliness and depression. This study aimed to: (1) investigate associations between loneliness, MIL, and depression, differentiated by sex in individuals aged ≥ 50 years, residing in 26 European countries and Israel, and (2) to examine in men and women separately whether MIL mediated the relationship between loneliness and depression. We included 41,372 individuals (23,789 women) who responded to wave 8 of the SHARE project. The variables analyzed were loneliness (UCLA), depression (EURO-D scale), and MIL (CASP-19). The analytical procedures included regression analysis and exploratory mediation analysis. Among men and women, the odds of loneliness increasing depression were 3.6 and 3.3 times higher, respectively. Among men, feeling MIL sometimes or often had odds for reducing depression by 0.53 and 0.21, respectively. In women, feeling MIL sometimes or frequently reduced the odds of depression by 0.37 and 0.19, respectively. Regardless of sex, mediation analyses showed a positive association between loneliness and depression, while MIL was negatively associated with loneliness and depression. MIL partially mediated the association between LON and depression in male and female models by approximately 83.2% and 80.7%, respectively. No differences were found between men's and women's mediation models. Regardless of sex, high levels of MIL seem to be effective in benefiting the mental health of Europeans aged 50 and over. MIL proved to be a significant mediator of the relationship between loneliness and depression, while loneliness and depression strengthened each other.