Frontiers in Psychiatry最新文献

Objective: To investigate the sleep status and mental health of migrants elderly who followed their children (MEFC) and analyze the influencing factors.

Methods: A total of 583 MEFCs were surveyed using a general demographic questionnaire, the Pittsburgh Sleep Quality Index (PSQI) scale, and the Symptom Checklist 90 (SCL90) scale.

Results: The mean PSQI score for MEFCs was 6.98 ± 0.17, and the average SCL90 score was 64.06 ± 2.03. Multiple linear regression analysis indicated that health status and adaptation to the migration destination were associated with PSQI scores (P < 0.05). Health status, adaptation to the migration destination, and family harmony were associated with SCL90 scores (P < 0.05). The association coefficient between the total PSQI and SCL90 scores was r=0.462 (P < 0.05).

Conclusion: The sleep and mental health of MEFCs were at a normal level. Health status and adaptation influenced sleep status, while health status, adaptation, and family harmony impacted mental health. However, the association between mental health and sleep status was weak.

Introduction: Depression is a serious mental health disease. Traditional scale-based depression diagnosis methods often have problems of strong subjectivity and high misdiagnosis rate, so it is particularly important to develop automatic diagnostic tools based on objective indicators.

Methods: This study proposes a deep learning method that fuses multimodal data to automatically diagnose depression using facial video and audio data. We use spatiotemporal attention module to enhance the extraction of visual features and combine the Graph Convolutional Network (GCN) and the Long and Short Term Memory (LSTM) to analyze the audio features. Through the multi-modal feature fusion, the model can effectively capture different feature patterns related to depression.

Results: We conduct extensive experiments on the publicly available clinical dataset, the Extended Distress Analysis Interview Corpus (E-DAIC). The experimental results show that we achieve robust accuracy on the E-DAIC dataset, with a Mean Absolute Error (MAE) of 3.51 in estimating PHQ-8 scores from recorded interviews.

Discussion: Compared with existing methods, our model shows excellent performance in multi-modal information fusion, which is suitable for early evaluation of depression.

Background: Approximately 70% of self-poisoning suicides occur in low- and middle-income countries (LMICs).The implementation of pesticide bans has significantly reduced the rate of pesticide self-poisoning in these regions; however, this has been accompanied by a shift toward an increased incidence of pharmaceutical poisoning, highlighting the importance of intervention strategies to prevent pharmaceutical self-poisoning in the future. This report summarizes the existing evidence on community pharmacist gatekeeper interventions aimed at reducing pharmaceutical suicide to discuss their complementary role with pesticide bans in LMICs.

Methods: The literature review identified studies published between April 2014 and April 2024 using multiple keywords related to "suicide," "intervention," "pharmacist" and "gatekeeper" in various library databases. Data were extracted into a table for analysis.

Results: Only eight relevant studies were found during the search period, and none quantified the impact of pharmacy gate keeper interventions. Community pharmacists became more confident and willing to intervene after pharmacist gatekeeper training. They demonstrated positive attitudes and improved knowledge and skills in responding to suicidal intent. However, the evidence supporting community pharmacy gatekeeper interventions primarily comes from developed countries. Furthermore, the role of pharmacists in preventing suicide relies on frequent contact between suicidal individuals and pharmacies in developed countries.

Conclusion: Pharmacy gatekeeper interventions can be implemented in LMICs as a complement to pesticide bans, provided they are modified and adapted to suit the specific context of these regions. Further research is essential to tailor and implement successful strategies from developed countries to address the unique challenges faced by LMICs.

Background: Diagnosing bipolar disorder poses a challenge in clinical practice and demands a substantial time investment. With the growing utilization of artificial intelligence in mental health, researchers are endeavoring to create AI-based diagnostic models. In this context, some researchers have sought to develop machine learning models for bipolar disorder diagnosis. Nevertheless, the accuracy of these diagnoses remains a subject of controversy. Consequently, we conducted this systematic review to comprehensively assess the diagnostic value of machine learning in the context of bipolar disorder.

Methods: We searched PubMed, Embase, Cochrane, and Web of Science, with the search ending on April 1, 2023. QUADAS-2 was applied to assess the quality of the literature included. In addition, we employed a bivariate mixed-effects model for the meta-analysis.

Results: 18 studies were included, covering 3152 participants, including 1858 cases of bipolar disorder. 28 machine learning models were encompassed. Sensitivity and specificity in discriminating between bipolar disorder and normal individuals were 0.88 (9.5% CI: 0.74~0.95) and 0.89 (95% CI: 0.73~0.96) respectively, and the SROC curve was 0.94(95% CI: 0.92~0.96). The sensitivity and specificity for distinguishing between bipolar disorder and depression were 0.84 (95%CI: 0.80~0.87) and 0.82 (95%CI: 0.75~0.88) respectively. The SROC curve was 0.89 (95%CI: 0.86~0.91).

Conclusions: Machine learning methods can be employed for discriminating and diagnosing bipolar disorder. However, in current research, they are predominantly utilized for binary classification tasks, limiting their progress in clinical practice. Therefore, in future studies, we anticipate the development of more multi-class classification tasks to enhance the clinical applicability of these methods.

Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023427290, identifier CRD42023427290.

Cultural biases and integration in novel socio-geographic contexts are relevant factors for the understanding of dynamics beneath sexual violence, and possibly play a role in modifying responsibility and perpetrators treatment. Here we offer a conceptual analysis of the relevant literature and two case study. Cultural factors influence power dynamics and individual values, impacting the occurrence of sexual violence; the understanding of "coercion" varies across cultures, and cultural legitimization may ensue. The consequences of sexual assault also differ, with shame prevailing in socio-centric societies and guilt in ego-centric societies. Rape and gender-based violence is influenced by masculinity and femininity concepts, the former identified with power. Dominance, rather than sexual gratification, can lead to sexual violence, which could also be a "male backlash" against gender equality. Biological theories link sexual violence to genetic factors; a psychodynamic perspective suggests an unconscious social reproduction of masculine culture and delves into possible explanations for violent behavior. Acculturation strategies and acculturative stress are explored, with a focus on Berry's strategies and on Camilleri's model of identity in intercultural situations. The impact of cultural factors on responsibility is discussed, highlighting variability in criminal laws and attitudes towards cultural offenses in different countries. The analysis of two criminal cases accused of rape, revealed common and diverging elements. Both individuals come from favorable socio-economic backgrounds, and lacked of prior or present psychiatric diagnoses. Integration difficulties, psychosexual attitudes, and the improper application of cultural codes played a crucial role. In conclusion, anthropological and ethnopsychiatric knowledge should be integrate into forensic assessments. Early detection of non-acculturation elements is need to prevent criminal behaviors, and a diagnostic instrument as a validated rating scale should be implemented.

The recurrence and treatment resistance of depression remain significant issues, primarily due to an inadequate understanding of its pathogenesis. Recent scientific evidence indicates that gut microbiota influence estradiol metabolism and are associated with the development of depression in nonpremenopausal women. Integrating existing studies on the regulation of estradiol metabolism by microorganisms in nature and the relevance of its degradation products to depression, recent scientific explorations have further elucidated the key mechanisms by which gut microbiota catabolize estradiol through specific metabolic pathways. These emerging scientific findings suggest that the unique metabolic effects of gut microbiota on estradiol may be one of the central drivers in the onset and course of depression in non-menopausal women.

Introduction: Autism Spectrum Disorder (ASD) presents significant challenges in social communication and interaction, critically impacting the lives of children with ASD. Traditional interventions, such as Applied Behavior Analysis (ABA) and Social Skills Training (SST), have been widely used to address social skill deficits in these children. While these methods are effective, they often require substantial resources, long-term engagement, and specialized expertise, which limit their accessibility and adaptability to diverse social contexts. Recent advancements in artificial intelligence (Al), particularly Transformer-based models, offer a novel opportunity to enhance and personalize social skills training.

Methods: This study introduces a Public Health-Driven Transformer (PHDT) model specifically designed to improve social skills in children with ASD. By integrating public health principles with state-of-the-art Al methodologies, the PHDT model creates interventions that are adaptable, accessible, and sensitive to individual needs. Leveraging multi-modal data inputs-such as text, audio, and facialcues-PHDT provides real-time social context interpretation and adaptive feedback, enabling a more naturalistic and engaging learning experience.

Results and discussion: Experimental results reveal that PHDT significantly outperforms traditional methods in fostering engagement, retention, and social skill acquisition. These findings highlight PHDT's potential to improve social competencies in children with ASD and to revolutionize access to specialized support within public health frameworks. This work underscores the transformative impact of Al-driven, public health-oriented interventions in promoting equitable access to essential developmental resources and enhancing the quality of life for children with ASD.

Introduction: Post-Traumatic Stress Disorder (PTSD) entails behavioral changes with increased risk of suicide, and there is no consensus on the preferred antidepressants for treatment of those PTSD patients who are at elevated risk for suicide.

Methods: We conducted a clinical trial emulation study comparing suicide-related events (SREs) among those patients' initiating antidepressants within 60 days after a qualifying SRE. Patients were followed from initiation of antidepressant until any of the following: treatment cessation, switching, death, or loss to follow-up. The outcome is a new onset of an SRE.

Results: Citalopram exhibited a significantly fewer case with new SREs compared to other most used antidepressants such as venlafaxine, duloxetine, and mirtazapine-even after adjusting for multiple comparisons and other covariants.

Discussion: Findings suggest potential risks associated with certain antidepressants in the PTSD population, emphasizing cautious prescription considerations.

Background: Despite numerous awareness campaigns and anti-stigma programs, people with mental illness, particularly schizophrenia, are still stigmatized. Although the society is both cause and solution, societal-level conditions, such as society's customs and policies that legitimize and perpetuate stigmatization is often neglected. We used a milieu approach to investigate how shared social, cultural and political orientations and expectations are associated with manifestations of the mental-illness related stigma.

Methods: We analyzed cross-sectional data from 3,042 adults aged ≥18 years from a national vignette-based representative survey on the stigma of mental illness in Germany from 2020. For milieu classification, we used an established population segmentation tool based on values and political preferences. Two stigma measures associated with the stereotype and status loss/discrimination components were assessed (i.e., the Social Distance Scale and a list of well-known stereotypes associated with depression or schizophrenia). Descriptive analyses and one-way ANOVAs with post-hoc pairwise contrasts between milieu groups were used to evaluate agreement on stereotypes and the desire for social distance towards people with depression or schizophrenia.

Results: Negative stereotypes about people with a depression (i.e., beliefs about being weak-willed) and schizophrenia (i.e., beliefs about dangerousness) tended to be more common in milieu groups leaning more toward the authoritarian pole. Milieu groups with a more liberal attitude on the socio-cultural dimension further expressed a lower desire for social distance towards people with depression (p<0.001). However, the extent of differentiation between the milieu groups was less pronounced regarding the desire for social distance towards people with schizophrenia than towards people with depression.

Conclusion: Our findings suggest that socio-cultural and socioeconomic dimensions of the society can be used for both describing heterogeneous societies and illuminating the underlying social structure of stigma. In addition to making blind spots more visible (i.e., schizophrenia), milieu-specific knowledge could be useful in deciding which intervention components are most appropriate for which milieu groups and how to apply them successfully.

Introduction: It is important to consider individual differences in research on educational technology. This study investigates the interplay between autistic traits, gender, and the perception of artificial intelligence (AI) tools designed for second language (L2) speaking practice, contributing to a deeper understanding of inclusive educational technology.

Methods: A sample of 111 university students completed the Broad Autism Phenotype Questionnaire (BAPQ) to measure autistic traits (AU) and their sub-traits Aloof (AF), Rigid (RD), and Pragmatic Language (PL). Perceptions of AI tools were assessed across five dimensions: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude (AT), Behavioral Intention (BI), and Usage Behavior (UB). The study utilized correlation and regression analyses to examine relationships between these variables, while exploring gender-specific moderating effects.

Results: Key findings revealed no significant gender differences in autistic traits or overall perceptions of AI tools. Contrary to expectations, autistic traits were negatively correlated with perceptions of AI tools, suggesting that current AI designs may not adequately support individuals with pronounced autistic traits. Additionally, gender moderated some relationships, with males displaying stronger associations between autistic traits and both PEOU and UB.

Discussion: This research bridges critical gaps by linking neurodiversity and gender to technology acceptance, advancing the field's understanding of individual differences in AI-based language learning. It underscores the importance of designing personalized and adaptive educational tools that address diverse learner needs, promoting inclusivity and effectiveness in L2 practice.