Search Results (2,392)

Suicide is often referred to as a silent killer, and the need to break down barriers and build bridges to communication and understanding remains of vital importance. Working within the field of further and higher education for more than 18 years with students experiencing suicidal thoughts, feelings, and behaviours has highlighted how often deep pain, grief, and trauma go unspoken, unseen, and unheard. Societal and cultural stigma, judgement, misunderstanding, and assumptions remain, all of which silence and can lead to a negative sense of self, others, and a person’s experience of being in the world. This article shows how using arts-based and visual research methods, as part of a mixed methods study, can offer unique insights into the inner world of lived experiences. It draws on analysis of 62 artworks made by 20 students between the ages of 16 and 25 with personal experiences of attempted suicide. These included two-dimensional pieces, sculpture, photography, poetry, and digital art. The research methodology is also discussed, including a 5/6-step approach to the analysis of visual data and data synthesis that has been created to ensure a robust, socially contextualised, and framed analysis. This follows polytextual thematic analysis using a multimodal approach and draws on visual social semiotics. Analysis of visual and arts-based data has revealed aspects of meaning that would otherwise not have been identified. This has led to the development of a model that can help us better understand the cycle of stigma and judgement and how we may be able to break it. This article demonstrates how a creative approach provides a means to share some of the complexity of feelings in a relatable way that has the capacity to bridge the divide between what is hidden and what is seen, bringing this human experience out of the shadows. It aims to honour everyone whose experiences have gone unseen, unspoken, and unheard, as well as the research participants’ wish for their artworks to be shared as a way to challenge the stigma that silences. It further hopes to demonstrate the power of arts-based and visual methods in research whilst also acknowledging some of their limitations so that they can be used more widely with under-represented, marginalised, and silenced voices. Full article

Background/Objectives: In recent years, there has been an increased interest in reducing suicide rates through dietary modification; however, the relationship between riboflavin intake and suicide risk remains unclear. This study aims to examine the association between dietary riboflavin and suicidal ideation. Methods: A total of 17,320 participants from the Korean National Health and Nutrition Examination Survey (KNHANES) 2014–2020 were included. Suicidal ideation was assessed using the ninth item of the Patient Health Questionnaire-9 (PHQ-9). Riboflavin intake was evaluated through dietary assessments. Multivariate logistic regression, restricted cubic spline (RCS) regression analysis, subgroup analysis, and interaction tests were conducted to explore the relationship between riboflavin intake and suicidal ideation. Results: There was a statistically significant association between riboflavin intake and suicidal ideation [OR (95%CI): 0.83 (0.77, 0.91), p < 0.001], after full adjustment for covariates. The linear trend test, using Q1 as the reference, showed ORs (95% CI) for Q2 and Q3 of 0.96 (0.81, 1.15) and 1.06 (0.80, 1.42), respectively. The RCS analysis revealed a non-linear pattern in the relationship between riboflavin intake and suicidal thoughts. This association was particularly significant among women and individuals younger than 60 years. Subgroup analyses and interaction tests indicated that the associations remained consistent across subgroups and were not influenced by factors other than anaerobic exercise. Conclusions: Our findings suggest a non-linear inverse relationship between riboflavin intake and suicidal ideation, with notable variations by sex and age. Modifying dietary riboflavin intake may be a crucial strategy for reducing suicide risk. Full article

Background/Objectives: Cognitive challenges in attention and executive function worsen over time in individuals with major depressive disorder (MDD) and suicidal risk. These difficulties persist beyond acute episodes, with limited targeted treatments available. Neurologic music therapy (NMT) is effective for cognitive rehabilitation in brain injuries and developmental disabilities, suggesting potential benefits for adults with MDD and suicide risk. This pilot study evaluated the feasibility, acceptability, and preliminary effectiveness of short-term NMT on cognitive function in adults with MDD. Methods: Adults aged 18+ with MDD and suicidal ideations participated in an 8-week single-arm open label study with 45-min individual in-person NMT sessions using musical attention control training (MACT) and musical executive function training (MEFT). Participants provided feedback on feasibility and acceptability, and pre- and post-intervention assessments included neurocognitive tasks and questionnaires on suicidal ideation, depressive symptoms, and quality of life. Results: A total of 18 individuals enrolled, and 10 participants completed the study protocol. Of the participants, 100% were satisfied with their experience with NMT, with 100% noting improvements in attention and 80% in executive function. Participants experienced some improvements in short-term memory (Digit Span Forward Test), cognitive flexibility (Trail Making Test B), and inhibitory control (Stroop Task). Significant reduction in suicidal ideation intensity (Beck Suicidal Scale of Ideation) was observed, as well as significant improvements in quality of life. Conclusions: This is the first study using NMT to demonstrate feasibility, acceptability, and effectiveness with respect to cognitive function in adults with MDD and suicide risk, providing preliminary data for future randomized controlled trials. Full article

The timely identification of suicidal ideation on social media is pivotal for global suicide prevention efforts. Addressing the challenges posed by the unstructured nature of social media data, we present a novel Chinese-based dual-channel model, DSI-BTCNN, which leverages deep learning to discern patterns indicative of suicidal ideation. Our model is designed to process Chinese data and capture the nuances of text locality, context, and logical structure through a fine-grained text enhancement approach. It features a complex parallel architecture with multiple convolution kernels, operating on two distinct task channels to mine relevant features. We propose an information gain-based IDFN fusion mechanism. This approach efficiently allocates computational resources to the key features associated with suicide by assessing the change in entropy before and after feature partitioning. Evaluations on a customized dataset reveal that our method achieves an accuracy of 89.64%, a precision of 92.84%, an F1-score of 89.24%, and an AUC of 96.50%, surpassing TextCNN and BiLSTM models by an average of 4.66%, 12.85%, 3.08%, and 1.66%, respectively. Notably, our proposed model has an entropy value of 81.75, which represents a 17.53% increase compared to the original DSI-BTCNN model, indicating a more robust detection capability. This enhanced detection capability is vital for real-time social media monitoring, offering a promising tool for early intervention and potentially life-saving support. Full article

Though transgender and gender diverse (TGD) youth are disproportionately affected by suicide and disordered eating, little research has explored the relationship between the two using state-level data. This exploratory study examined whether disordered eating behaviors moderate the observed relationship between gender and suicide among adolescents. Multivariate logistic regression was performed on the population-based 2023 Healthy Kids Colorado Survey (HKCS) data (N = 49,989) to test whether the odds of suicide ideation and attempt differed by gender groups, and modeling was carried out to examine the moderation of these relationships by disordered eating behaviors. Additionally, analyses were replicated among only gender minority groups (n = 2486). Compared to cismale students, the prevalence of disordered eating was higher among all other gender identities. There was a significantly higher risk of suicidal ideation and attempts among transfemale, transmale, nonbinary and gender-questioning students. Disordered eating did not significantly moderate the relationship between gender and suicide outcomes. These findings underscore the heightened vulnerability of TGD youth to disordered eating and suicidal behaviors while suggesting that disordered eating may not be a pathway through which gender relates to suicide outcomes. Full article

Background/Objectives: Voluntary Assisted Dying in Australia was first legislated in 2019, with significant concern expressed by palliative care services about the impact on services. We aimed to describe the impact of Voluntary Assisted Dying on community-based palliative care client care. Methods: This study was a retrospective cohort study that compared the characteristics and outcomes of clients who expressed interest in VAD, those who chose voluntary assisted death, and the broader client population of the service. Results: Only 4% of the total client population expressed interest in VAD, and 1% died through VAD. Of the clients who expressed interest in VAD, most had malignancy as their primary diagnosis. The median length of palliative care service for clients who expressed interest in VAD was 101 days, compared to 48 days for all service clients. For 97% of individuals who died from taking the substance, death occurred in their place of choice compared with 71% of all service clients. Of the clients who died through VAD, 88% of these deaths occurred in a community setting compared with 56% of all service clients. Conclusions: Most clients who took the VAD medicine died in their place of choice, which was the community. A review of the length of engagement with the service indicated that a longer length of engagement was illustrated by individuals navigating the VAD process. This study emphasised the value of early referral to community-based palliative care, enabling a focus on quality of life, symptom management, and planning for death. Full article

This study examines the association between problematic internet use, or internet addiction, and adolescent mental health, focusing on key psychological variables, assessing the strength of these associations, and identifying potential moderating factors. Methods: A search of the Web of Science databases over the past five years identified 830 articles. Of these, 33 met the inclusion criteria, involving 303,243 participants (average age 14.57; 49.44% female). The selection process was verified by two researchers. Results: Nine psychological variables were analyzed: depression, anxiety, stress, suicidal behaviour, psychological well-being, self-esteem, externalizing problems, aggressiveness, and impulsiveness. Internet addiction showed positive correlations with aggressiveness (r+ = 0.391), depression (r+ = 0.318), anxiety (r+ = 0.252), and suicidal behaviour (r+ = 0.264). Negative correlations were observed with psychological well-being (r+ = −0.312) and self-esteem (r+ = −0.306). No significant associations were found for externalizing problems, impulsiveness, or stress. None of the moderators showed a significant correlation with internet addiction and depression. Conclusions: Although limited by small sample sizes for some variables and the cross-sectional design of most studies, the findings confirm that there is a negative relationship between internet addiction and adolescent mental health. It is related to poorer self-perceived health, greater psychological distress, and greater aggression. Full article

This study analyzed the effect of the COVID-19 pandemic on suicide rates among Brazilian residents, stratified by sex. It examined monthly suicide rates using interrupted time series analysis. Researchers compared the months before the pandemic (January 2017 to February 2020) with those after the first diagnosed case of COVID-19 in Brazil (March 2020 to December 2023). They applied an interrupted time series model (quasi-Poisson) to account for serial autocorrelation in the residuals and seasonality. During this period, authorities reported 102,081 suicides in Brazil. The age-standardized annual suicide rate among men was 3.71 times higher than the rate among women (12.33 suicides per 100,000 vs. 3.32 suicides per 100,000 women). The South and Midwest regions had the highest standardized annual average suicide rates. Suicide rates among men dropped abruptly at the pandemic’s onset (RR < 1, p < 0.05). However, Black men, women aged 15 to 19 years, and elderly individuals exhibited a significant increase (RR > 1, p < 0.05). Over time, suicide rates rose across most age groups, regions, and methods studied (RR > 1, p < 0.05). The pandemic’s impact differed significantly between men and women based on stratification variables. Nonetheless, a progressive upward trend emerged throughout the pandemic. Full article

Background and Objective: During the COVID-19 pandemic, women faced unique employment-related stressors, including higher exposure to unstable working conditions, increased workload changes due to motherhood, and greater risk of infection in certain jobs. This study explores how these factors influence women’s anxiety and subjective well-being, aiming to identify vulnerable groups. Materials and Methods: 230 employed Slovene women, aged from 19 to 64 years (M = 32.60, SD = 10.41), participated in an online survey containing a State-Trait Anxiety Inventory (STAI-6), WHO-5 Well-being Index, and a set of questions regarding their occupation and demographic profile. Hierarchical linear regressions and chi-squared tests were performed. Results: The risk of job or income loss significantly predicted an increase in anxiety levels. However, despite fear of infection, none of the work-related variables predicted a significant decrease in subjective well-being. Women reporting risk of job or income loss are predominantly those with lower education and income, working students, self-employed, or working in the private sector. Conclusions: Employment insecurity is an important contributor to anxiety in women. The findings highlight the need to ensure job security, particularly for women working in precariat working conditions, as their work and economic stability prove to be vulnerable to external economic disturbances. Full article

Background: Studies have shown that gay men experience higher levels of depression and are more likely to report suicidal ideation, plans, and attempts over their lifetime compared to heterosexual men. However, most studies have been conducted with adolescents and young adults, while there is a lack of research focusing on older adults. The aims of this study are to assess the level of depressive symptoms among older gay men and examine the associations between five key factors—loneliness, internalized homophobia, self-esteem, ageism, health behavior—and depressive symptoms. Methods: The convenience sample included seventy-nine gay men living in the community. Prospective participants were recruited by facilitators of social and support groups, who either distributed the questionnaire directly to members on-site or forwarded a link to their emails. All study measures used were valid and reliable. Results: Participants’ mean level of depression exceeded the scale’s cutoff point for detecting depression, indicating mild depression. Four variables made a significant contribution to the explanation of depression, with loneliness having the largest contribution, followed by ageism, internalized homophobia, and health behavior. The regression model explained a very high percentage of the depression variance (83%). Conclusions: These four factors are central to understanding depression among older gays. Medical and social professionals should recognize their significance and incorporate them into the treatment provided to those in need. Further studies are needed to gain a deeper understanding of the factors associated with depression in this vulnerable population. Full article

As suicide rates increase globally, there is a growing need for effective, data-driven methods in mental health monitoring. This study leverages advanced artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), to identify suicidal ideation from Twitter data. A predictive model was developed to process social media posts in real time, using NLP and sentiment analysis to detect textual and emotional cues associated with distress. The model aims to identify potential suicide risks accurately, while minimizing false positives, offering a practical tool for targeted mental health interventions. The study achieved notable predictive performance, with an accuracy of 85%, precision of 88%, and recall of 83% in detecting potential suicide posts. Advanced preprocessing techniques, including tokenization, stemming, and feature extraction with term frequency–inverse document frequency (TF-IDF) and count vectorization, ensured high-quality data transformation. A random forest classifier was selected for its ability to handle high-dimensional data and effectively capture linguistic and emotional patterns linked to suicidal ideation. The model’s reliability was supported by a precision–recall AUC score of 0.93, demonstrating its potential for real-time mental health monitoring and intervention. By identifying behavioral patterns and triggers, such as social isolation and bullying, this framework provides a scalable and efficient solution for mental health support, contributing significantly to suicide prevention strategies worldwide. Full article

Suicidality is among the most controversial concepts in multi-disciplinary studies worldwide, regardless of the form and approach. The etiopathological variability in suicidal ideation correlates with the heterogeneity of the clinical and behavioural patterns of self-harm attempts, which significantly impact the prognosis and quality of life of patients. The main objective of the present study was to identify and outline the spectrum of factors predisposing to suicide, with the whole suite of consequences and manifestations in ideation and behaviour. In this regard, the research literature of the last decade contains numerous articles dealing with the theoretical premises pertaining to both the statistical and the profoundly psychological and philosophical dimensions of suicide. The micro-environment favouring the clinical evolution of self-harm/self-destructive thoughts and attempts to the terminal, final act integrates individual medical-biological and psychological factors into the overall social reality. Knowledge of the whole etiopathogenic amalgam with clinical-evolutionary implications allows for the development of methods and tools for the early assessment and prevention of suicidal risk. At the same time, the present study aims to qualitatively focus on the subjective motivation declared by patients regarding the internal, individual catalyst of suicidal ideation and attempts on a predominantly psycho-social coordination. Full article

Large language models (LLMs) offer promising possibilities in mental health, yet their ability to assess disorders and recommend treatments remains underexplored. This quantitative cross-sectional study evaluated four LLMs (Gemini (Gemini 2.0 Flash Experimental), Claude (Claude 3.5 Sonnet), ChatGPT-3.5, and ChatGPT-4) using text vignettes representing conditions such as depression, suicidal ideation, early and chronic schizophrenia, social phobia, and PTSD. Each model’s diagnostic accuracy, treatment recommendations, and predicted outcomes were compared with norms established by mental health professionals. Findings indicated that for certain conditions, including depression and PTSD, models like ChatGPT-4 achieved higher diagnostic accuracy compared to human professionals. However, in more complex cases, such as early schizophrenia, LLM performance varied, with ChatGPT-4 achieving only 55% accuracy, while other LLMs and professionals performed better. LLMs tended to suggest a broader range of proactive treatments, whereas professionals recommended more targeted psychiatric consultations and specific medications. In terms of outcome predictions, professionals were generally more optimistic regarding full recovery, especially with treatment, while LLMs predicted lower full recovery rates and higher partial recovery rates, particularly in untreated cases. While LLMs recommend a broader treatment range, their conservative recovery predictions, particularly for complex conditions, highlight the need for professional oversight. LLMs provide valuable support in diagnostics and treatment planning but cannot replace professional discretion. Full article

Intimate partner violence (IPV) is a public health and social problem worldwide. However, most studies have concentrated on violence against women and not also against men. Interventions for victimized men will only be successful if there is a better understanding of the real experiences, as narrated by the victims themselves, and how these impact their health and wellbeing. This study aimed to investigate the experiences of intimate partner violence, health, and wellbeing among men in east-central Sweden. Data were gathered using eleven in-depth, semi-structured interviews with men who were victims of IPV. Four categories emerged from the analyses: experiences of abuse in the relationship; feelings of isolation, loneliness, and shame; perceived deterioration of health and wellbeing; and negative experiences with public services. The findings indicate that interviewees experienced psychological (rather than physical) violence at the hands of their intimate partner. The abuse had consequences for their health and wellbeing, as they experienced stress, anxiety, depression, and suicidal thoughts. In some instances, it affected their health behavior, as they reverted to alcohol and drug use to cope with the abuse. Moreover, the interviewees felt lonely and unwilling to disclose their suffering because of fear of what family, friends, society, and professionals across different services would think of them. Also, they experienced negative responses from the health and social care services and police when seeking help, which made them even more entrenched in their fear of disclosing the suffering caused by the abuse. Full article

Background: Mental disorders are disturbances of brain functions that cause cognitive, affective, volitional, and behavioral functions to be disrupted to varying degrees. One of these disorders is depression, a significant factor contributing to the increase in suicide cases worldwide. Consequently, depression has become a significant public health issue globally. Electroencephalogram (EEG) data can be utilized to diagnose mild depression disorder (MDD), offering valuable insights into the pathophysiological mechanisms underlying mental disorders and enhancing the understanding of MDD. Methods: This survey emphasizes the critical role of EEG in advancing artificial intelligence (AI)-driven approaches for depression diagnosis. By focusing on studies that integrate EEG with machine learning (ML) and deep learning (DL) techniques, we systematically analyze methods utilizing EEG signals to identify depression biomarkers. The survey highlights advancements in EEG preprocessing, feature extraction, and model development, showcasing how these approaches enhance the diagnostic precision, scalability, and automation of depression detection. Results: This survey is distinguished from prior reviews by addressing their limitations and providing researchers with valuable insights for future studies. It offers a comprehensive comparison of ML and DL approaches utilizing EEG and an overview of the five key steps in depression detection. The survey also presents existing datasets for depression diagnosis and critically analyzes their limitations. Furthermore, it explores future directions and challenges, such as enhancing diagnostic robustness with data augmentation techniques and optimizing EEG channel selection for improved accuracy. The potential of transfer learning and encoder-decoder architectures to leverage pre-trained models and enhance diagnostic performance is also discussed. Advancements in feature extraction methods for automated depression diagnosis are highlighted as avenues for improving ML and DL model performance. Additionally, integrating Internet of Things (IoT) devices with EEG for continuous mental health monitoring and distinguishing between different types of depression are identified as critical research areas. Finally, the review emphasizes improving the reliability and predictability of computational intelligence-based models to advance depression diagnosis. Conclusions: This study will serve as a well-organized and helpful reference for researchers working on detecting depression using EEG signals and provide insights into the future directions outlined above, guiding further advancements in the field. Full article