Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks

Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment. Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention. However, classifying suicidal ideation and other mental disorders is challenging as they share similar patterns in language usage and sentimental polarity. This paper enhances text representation with lexicon-based sentiment scores and latent topics and proposes using relation networks to detect suicidal ideation and mental disorders with related risk indicators. The relation module is further equipped with the attention mechanism to prioritize more critical relational features. Through experiments on three real-world datasets, our model outperforms most of its counterparts

Preventing suicide by young people

Introduction In 2013, 2,522 people died by suicide in Australia. Twenty-two of these were children aged 5- 14 years, 148 were adolescents aged 15-19 years, and a further 200 were young people aged 20-24 years. Although the suicide rate for children and adolescents is lower than that for some older age groups, suicide is the leading cause of death in children and young people. Suicide has immense effects on the families, friends, and communities of people who die by suicide, causing long lasting grief and guilt. Arguably, these effects are even greater when the person who died by suicide is young. It is estimated that suicide costs the Australian economy more than $17 billion per year. Researchers and policy makers recognise that suicide is preventable, yet suicide rates have changed little in the past 10 years. This discussion paper aims to focus a spotlight on the unique experience of young people. It does this by providing a critical analysis of existing policy and evidence based responses relevant to young people

Knowledge Transferring via Model Aggregation for Online Social Care

The Internet and the Web are being increasingly used in proactive social care to provide people, especially the vulnerable, with a better life and services, and their derived social services generate enormous data. However, the strict protection of privacy makes user's data become an isolated island and limits the predictive performance of standalone clients. To enable effective proactive social care and knowledge sharing within intelligent agents, this paper develops a knowledge transferring framework via model aggregation. Under this framework, distributed clients perform on-device training, and a third-party server integrates multiple clients' models and redistributes to clients for knowledge transferring among users. To improve the generalizability of the knowledge sharing, we further propose a novel model aggregation algorithm, namely the average difference descent aggregation (AvgDiffAgg for short). In particular, to evaluate the effectiveness of the learning algorithm, we use a case study on the early detection and prevention of suicidal ideation, and the experiment results on four datasets derived from social communities demonstrate the effectiveness of the proposed learning method

A randomized controlled pilot trial of classroom-based mindfulness meditation compared to an active control condition in sixth-grade children

The current study is a pilot trial to examine the effects of a nonelective, classroom-based, teacher-implemented, mindfulness meditation intervention on standard clinical measures of mental health and affect in middle school children. A total of 101 healthy sixth-grade students (55 boys, 46 girls) were randomized to either an Asian history course with daily mindfulness meditation practice (intervention group) or an African history course with a matched experiential activity (active control group). Self-reported measures included the Youth Self Report (YSR), a modified Spielberger State-Trait Anxiety Inventory, and the Cognitive and Affective Mindfulness Measure –Revised. Both groups decreased significantly on clinical syndrome subscales and affect but did not differ in the extent of their improvements. Meditators were significantly less likely to develop suicidal ideation or thoughts of self-harm than controls. These results suggest that mindfulness training may yield both unique and non-specific benefits that are shared by other novel activities

Implementation of a safety planning intervention on an inpatient psychiatric unit.

Background: In 2017, suicide was the 10th leading cause of death in the United States. There are multiple interventions used for individuals at risk for suicide. No-suicide contracts are commonly used by clinicians who treat individuals at high risk for suicide, but there is no evidence supporting this intervention in preventing suicides. A safety planning intervention has been shown to decrease the risk of suicide in individuals at high risk for suicide by helping individuals recognize warning signs of a suicidal crisis, use their internal coping strategies, and contact social network or community resources to assist in reducing suicidal thoughts. Safety plans provide individuals with tools to help manage crisis situations. Purpose: To address the risk of suicide in patients on an inpatient psychiatric unit at a community hospital, the focus of this DNP evidence-based practice (EBP) project was twofold: to implement a safety planning intervention for all patients found to be at high risk for suicide, and to evaluate the efficacy of the safety plan intervention among nurses (RNs and LPNs). Methods: An educational module was developed for this project on the use of safety plans and how to create individualized safety plans with patients. Nurses completed a demographic questionnaire and pre-test prior to receiving education on risks of suicide and safety planning, and post-test following the completion of educational module. The sample size consisted of 13 nurses. A Wilcoxon signed-rank test was used to assess knowledge level of the nurses from pretest to posttest on suicide risk factors and use of safety plans. To evaluate the efficacy of the safety planning intervention, descriptive statistics (frequencies/percentages) were used to determine if all patients at high-risk for suicide completed safety plans; if safety plans were fully completed; and if the patients were given a copy of the safety plan at discharge. A post implementation questionnaire was provided to nurses at six weeks post intervention to elicit evaluative information on the effectiveness of the safety plan intervention from the nurse’s perspective. Findings: A Wilcoxon Signed-Ranks Test indicated that post-test scores (Mean= 66.15) were statistically significantly higher than pre-test scores (Mean= 87.69, Z=36, p=.011). There were 58 patients admitted to the psychiatric unit during this project, with 47% (n=27) found to be at high-risk for suicide per the Columbia-Suicide Severity Rating Scale (C-SSRS). Of the 27 patients found to be at high-risk for suicide, 81% (n=22) completed a safety plan and 78% (n=21) had a safety plan included in their discharge instructions. Of the 13 nurses, 85% (n=11) strongly agreed that they felt confident in creating safety plans with patients at high risk for suicide and 77% (n=10) strongly agreed that they were satisfied with the practice change of implementing safety plans as part of their plan of care for patients at high risk for suicide. Implications: This project supports the implementation of an educational module to increase nurses’ knowledge of suicide risk and safety plan implementation on an inpatient psychiatric unit. Education of suicide risk and safety plan implementation is important to ensure nurses feel confident in using safety plans with patients at high-risk for suicide. Future studies should be done to evaluate the efficacy of safety plans on decreasing suicide risk

Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes

Recent statistics in suicide prevention show that people are increasingly posting their last words online and with the unprecedented availability of textual data from social media platforms researchers have the opportunity to analyse such data. Furthermore, psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. In this paper, we investigate whether it is possible to automatically identify suicide notes from other types of social media blogs in two document-level classification tasks. The first task aims to identify suicide notes from depressed and blog posts in a balanced dataset, whilst the second experiment looks at how well suicide notes can be classified when there is a vast amount of neutral text data, which makes the task more applicable to real-world scenarios. Furthermore we perform a linguistic analysis using LIWC (Linguistic Inquiry and Word Count). We present a learning model for modelling long sequences in two experiment series. We achieve an f1-score of 88.26% over the baselines of 0.60 in experiment 1 and 96.1% over the baseline in experiment 2. Finally, we show through visualisations which features the learning model identifies, these include emotions such as love and personal pronouns

“It’s cool to feel sad”: A thematic analysis of the social media experiences of university students who have self-harmed

Background: Suicide and self-harm rates amongst young people have been identified as a public mental health concern with emerging links to social media use. Aim: The current study aimed to qualitatively explore the social media experiences of university students who have self-harmed, as they have been identified as a group vulnerable to suicide. Method: Semi-structured interviews were completed at two time points with students aged 21 and under who have self-harmed whilst at university, with transcripts of interviews analysed using reflexive thematic analysis (Braun & Clarke, 2021). Results: Three organising themes were identified: 1) Instagram vs reality, 2) A double-edged sword, 3) Curating online spaces. The analysis provided a developmental overview of patterns across students’ experiences online, identifying negative social comparisons, the romanticisation of mental illness, and the development of their insight and rules to engage with social media in a helpful way. Conclusions: The study provided an insight into the evolution of the online lives of students who have self-harmed, highlighting key modifiable risk factors that researchers, policymakers and clinicians could meaningfully target to promote ‘digital hygiene’ and the reduction of potential harm from social media