scholarly journals Characterisation of mental health conditions in social media using Informed Deep Learning

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
George Gkotsis ◽  
Anika Oellrich ◽  
Sumithra Velupillai ◽  
Maria Liakata ◽  
Tim J. P. Hubbard ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Mental Illness ◽  
Deep Learning ◽  
Well Being ◽  
Health Conditions ◽  
Targeted Interventions ◽  
Mental Health Conditions ◽  
Life Years ◽  
Health And Social Care

Abstract The number of people affected by mental illness is on the increase and with it the burden on health and social care use, as well as the loss of both productivity and quality-adjusted life-years. Natural language processing of electronic health records is increasingly used to study mental health conditions and risk behaviours on a large scale. However, narrative notes written by clinicians do not capture first-hand the patients’ own experiences, and only record cross-sectional, professional impressions at the point of care. Social media platforms have become a source of ‘in the moment’ daily exchange, with topics including well-being and mental health. In this study, we analysed posts from the social media platform Reddit and developed classifiers to recognise and classify posts related to mental illness according to 11 disorder themes. Using a neural network and deep learning approach, we could automatically recognise mental illness-related posts in our balenced dataset with an accuracy of 91.08% and select the correct theme with a weighted average accuracy of 71.37%. We believe that these results are a first step in developing methods to characterise large amounts of user-generated content that could support content curation and targeted interventions.

scholarly journals Erratum: Corrigendum: Characterisation of mental health conditions in social media using Informed Deep Learning

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
George Gkotsis ◽  
Anika Oellrich ◽  
Sumithra Velupillai ◽  
Maria Liakata ◽  
Tim J. P. Hubbard ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Deep Learning ◽  
Health Conditions ◽  
Mental Health Conditions

scholarly journals Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis (Preprint)

2020 ◽  
Author(s):  
Xiaofeng Wang ◽  
Shuai Chen ◽  
Tao Li ◽  
Wanting Li ◽  
Yejie Zhou ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Deep Learning ◽  
Risk Prediction ◽  
Health Conditions ◽  
Learning Methods ◽  
Mental Health Conditions ◽  
Depression Risk ◽  
Language Representation ◽  
Using Data

BACKGROUND Depression is a serious personal and public mental health problem. Self-reporting is the main method used to diagnose depression and to determine the severity of depression. However, it is not easy to discover patients with depression owing to feelings of shame in disclosing or discussing their mental health conditions with others. Moreover, self-reporting is time-consuming, and usually leads to missing a certain number of cases. Therefore, automatic discovery of patients with depression from other sources such as social media has been attracting increasing attention. Social media, as one of the most important daily communication systems, connects large quantities of people, including individuals with depression, and provides a channel to discover patients with depression. In this study, we investigated deep-learning methods for depression risk prediction using data from Chinese microblogs, which have potential to discover more patients with depression and to trace their mental health conditions. OBJECTIVE The aim of this study was to explore the potential of state-of-the-art deep-learning methods on depression risk prediction from Chinese microblogs. METHODS Deep-learning methods with pretrained language representation models, including bidirectional encoder representations from transformers (BERT), robustly optimized BERT pretraining approach (RoBERTa), and generalized autoregressive pretraining for language understanding (XLNET), were investigated for depression risk prediction, and were compared with previous methods on a manually annotated benchmark dataset. Depression risk was assessed at four levels from 0 to 3, where 0, 1, 2, and 3 denote no inclination, and mild, moderate, and severe depression risk, respectively. The dataset was collected from the Chinese microblog Weibo. We also compared different deep-learning methods with pretrained language representation models in two settings: (1) publicly released pretrained language representation models, and (2) language representation models further pretrained on a large-scale unlabeled dataset collected from Weibo. Precision, recall, and F1 scores were used as performance evaluation measures. RESULTS Among the three deep-learning methods, BERT achieved the best performance with a microaveraged F1 score of 0.856. RoBERTa achieved the best performance with a macroaveraged F1 score of 0.424 on depression risk at levels 1, 2, and 3, which represents a new benchmark result on the dataset. The further pretrained language representation models demonstrated improvement over publicly released prediction models. CONCLUSIONS We applied deep-learning methods with pretrained language representation models to automatically predict depression risk using data from Chinese microblogs. The experimental results showed that the deep-learning methods performed better than previous methods, and have greater potential to discover patients with depression and to trace their mental health conditions.

scholarly journals Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis

10.2196/17958 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e17958
Author(s):  
Xiaofeng Wang ◽  
Shuai Chen ◽  
Tao Li ◽  
Wanting Li ◽  
Yejie Zhou ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Deep Learning ◽  
Risk Prediction ◽  
Health Conditions ◽  
Learning Methods ◽  
Mental Health Conditions ◽  
Depression Risk ◽  
Language Representation ◽  
Using Data

Background Depression is a serious personal and public mental health problem. Self-reporting is the main method used to diagnose depression and to determine the severity of depression. However, it is not easy to discover patients with depression owing to feelings of shame in disclosing or discussing their mental health conditions with others. Moreover, self-reporting is time-consuming, and usually leads to missing a certain number of cases. Therefore, automatic discovery of patients with depression from other sources such as social media has been attracting increasing attention. Social media, as one of the most important daily communication systems, connects large quantities of people, including individuals with depression, and provides a channel to discover patients with depression. In this study, we investigated deep-learning methods for depression risk prediction using data from Chinese microblogs, which have potential to discover more patients with depression and to trace their mental health conditions. Objective The aim of this study was to explore the potential of state-of-the-art deep-learning methods on depression risk prediction from Chinese microblogs. Methods Deep-learning methods with pretrained language representation models, including bidirectional encoder representations from transformers (BERT), robustly optimized BERT pretraining approach (RoBERTa), and generalized autoregressive pretraining for language understanding (XLNET), were investigated for depression risk prediction, and were compared with previous methods on a manually annotated benchmark dataset. Depression risk was assessed at four levels from 0 to 3, where 0, 1, 2, and 3 denote no inclination, and mild, moderate, and severe depression risk, respectively. The dataset was collected from the Chinese microblog Weibo. We also compared different deep-learning methods with pretrained language representation models in two settings: (1) publicly released pretrained language representation models, and (2) language representation models further pretrained on a large-scale unlabeled dataset collected from Weibo. Precision, recall, and F1 scores were used as performance evaluation measures. Results Among the three deep-learning methods, BERT achieved the best performance with a microaveraged F1 score of 0.856. RoBERTa achieved the best performance with a macroaveraged F1 score of 0.424 on depression risk at levels 1, 2, and 3, which represents a new benchmark result on the dataset. The further pretrained language representation models demonstrated improvement over publicly released prediction models. Conclusions We applied deep-learning methods with pretrained language representation models to automatically predict depression risk using data from Chinese microblogs. The experimental results showed that the deep-learning methods performed better than previous methods, and have greater potential to discover patients with depression and to trace their mental health conditions.

scholarly journals Emotional demands, compassion and mental health in social workers

2020 ◽  
Vol 70 (2) ◽  
pp. 89-94
Author(s):  
G Kinman ◽  
L Grant
Keyword(s):  
Mental Health ◽  
Social Workers ◽  
Compassion Fatigue ◽  
Social Care ◽  
Compassion Satisfaction ◽  
Well Being ◽  
Targeted Interventions ◽  
Self Compassion ◽  
Health And Social Care ◽  
Emotional Demands

Abstract Background Compassion, described as the act of providing care based on empathy, dignity and respect, is intrinsic to effective health and social care. Although delivering compassionate care has wide-ranging benefits for service users, more insight is needed into its effects on health and social care professionals. The emotional demands of ‘helping’ work can engender compassion fatigue that may impair well-being, whereas compassion satisfaction and feelings of compassion towards the self could be protective. Aims To examine the effects (direct and indirect) of compassion satisfaction, compassion fatigue and self-compassion on mental health in a cohort of social workers. Methods We used validated scales to measure emotional demands, compassion satisfaction and fatigue, and self-compassion and the General Health Questionnaire-12 to assess mental health. We tested the main and moderating effects of emotional demands and the three facets of compassion using hierarchical regression analysis. Results The study sample comprised 306 social workers (79% female). Participants who reported higher levels of compassion satisfaction and self-compassion tended to report better mental health, whereas compassion fatigue was a significant risk factor for well-being. The models explained 44–53% of the variance in mental health symptoms. We found some evidence that compassion satisfaction and self-compassion buffer the negative effects of emotional demand on mental health, contributing 2 and 3%, respectively, to the incremental variance. Conclusions Our findings suggest that evidence-based interventions are needed to reduce compassion fatigue and enhance compassion satisfaction and self-compassion in social care work. We consider ways to accomplish this using targeted interventions.

Police Violence among Adults Diagnosed with Mental Disorders

2020 ◽  
Vol 45 (2) ◽  
pp. 81-89
Author(s):  
Hyun-Jin Jun ◽  
Jordan E DeVylder ◽  
Lisa Fedina
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
General Population ◽  
Mental Disorders ◽  
Severe Mental Illness ◽  
Violence Exposure ◽  
Health Conditions ◽  
Psychotic Symptoms ◽  
Police Violence ◽  
Mental Health Conditions

Abstract Police violence is reportedly common among those diagnosed with mental disorders characterized by the presence of psychotic symptoms or pronounced emotional lability. Despite the perception that people with mental illness are disproportionately mistreated by the police, there is relatively little empirical research on this topic. A cross-sectional general population survey was administered online in 2017 to 1,000 adults in two eastern U.S. cities to examine the relationship between police violence exposure, mental disorders, and crime involvement. Results from hierarchical logistic regression and mediation analyses revealed that a range of mental health conditions are broadly associated with elevated risk for police violence exposure. Individuals with severe mental illness are more likely than the general population to be physically victimized by police, regardless of their involvement in criminal activities. Most of the excess risk of police violence exposure related to common psychiatric diagnoses was explained by confounding factors including crime involvement. However, crime involvement may necessitate more police contact, but does not necessarily justify victimization or excessive force (particularly sexual and psychological violence). Findings support the need for adequate training for police officers on how to safely interact with people with mental health conditions, particularly severe mental illness.

Effectiveness study of “occupational connections” – A short-term, in-patient intervention for promotion functioning and participation in daily life of people with mental health conditions

2017 ◽  
Vol 41 (S1) ◽  
pp. S380-S381
Author(s):  
L. Lipskaya-Velikovsky ◽  
T. Krupa ◽  
M. Kotler
Keyword(s):  
Mental Health ◽  
Functional Capacity ◽  
Daily Life ◽  
Well Being ◽  
The Body ◽  
Health Conditions ◽  
Hospital Treatment ◽  
Control Group ◽  
Study Group ◽  
Mental Health Conditions

ObjectivesMental health conditions (MHC) have been associated with restrictions in daily life participation and functioning affecting health and well-being. Substantial numbers of people with MHC experience hospitalizations, however, there is limited evidence supporting functional interventions in the in-patient setting to promote recovery. The OC is an intervention implemented during sub-acute hospitalization, which attempts to promote activity and participation of people with MHC, both during the in-patient stay and upon return to the community, with a view to enabling recovery. To facilitate its implementation, we investigate the OC effectiveness.AimsInvestigate the OC contribution to cognition, symptoms and functional capacity among inpatients with schizophrenia.MethodsThis is a quasi-experimental, prospective, pre/post-designed study with convenience sampling. Inpatients with schizophrenia were enrolled into the study group participating in the OC intervention (n = 16); or the control group participating in hospital treatment as usual (n = 17). The study participants completed evaluations at baseline and at discharge or after 10 weeks with: Neurocognitive State Examination, Trail Making Test, Ray Complex Figure, and Category Fluency Test for aspects of cognition; Positive and Negative Syndrome Scale for symptoms severity, and Observed Tasks of Daily Living-Revised for functional capacity.ResultsStatistically significant improvement in cognitive functioning, symptoms severity and functional capacity was found in the study group after the intervention. These changes were not observed in the control group.ConclusionThe results support the OC effectiveness for cognitive and functional capacity improvement and symptomology relief. The findings advance the body of evidence for functional interventions in hospital settings.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Interactive Activities to Aid in a Comprehensive Understanding of Mental Health Within the Professional Athletic Training Curriculum

2021 ◽  
Vol 16 (4) ◽  
pp. 262-269
Author(s):  
Jennifer Lynn Ostrowski ◽  
Ashley Gray ◽  
Ellen K. Payne ◽  
David Wilkenfeld ◽  
James R. Scifers
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Athletic Training ◽  
First Aid ◽  
Health Conditions ◽  
Standardized Patient ◽  
Counseling Session ◽  
Mental Health First Aid ◽  
Athletic Training Students ◽  
Mental Health Conditions

Context Mental health is a significant issue in the United States, with approximately 18.5% of adults and 22% of adolescents having a diagnosable mental illness. Athletic trainers are in a prime position to recognize signs of mental health illness in their patients and to facilitate referral to a mental health professional. Objective To introduce interactive approaches for developing mental health first aid and referral skills in professional athletic training students. Background Although the 2020 Commission on Accreditation of Athletic Training Education curricular content standards require programs to educate students about identification and referral for mental health conditions, the standards do not provide specific suggestions for instructing this content. Description Three educational activities have been implemented into the curriculum: an exploratory counseling session, Mental Health First Aid certification, and a standardized patient encounter. Clinical Advantage(s) The exploratory counseling session improves athletic training students' empathy for individuals living with mental illness. The Mental Health First Aid curriculum has been shown to raise awareness of mental health conditions and positively influence the number of people who receive professional help. Standardized patient scenarios have been shown to increase critical thinking and confidence with mental health cases and to increase knowledge in mental health assessment. Conclusion(s) Students' reflections support that these components have increased their competence and confidence in recognizing, intervening, and making referrals for individuals with suspected mental health concerns.

scholarly journals Overlooked and Unaddressed: A narrative review of mental health consequences of child marriages.

2021 ◽  
Author(s):  
Rochelle Ann Burgess ◽  
Mairi Jeffery ◽  
Sabina Odero ◽  
Kelly Rose-Clarke ◽  
Delanjathan Devakumar
Keyword(s):  
Mental Health ◽  
Emotional Distress ◽  
Partner Violence ◽  
Well Being ◽  
Poor Mental Health ◽  
Health Conditions ◽  
Child Marriage ◽  
Health Consequences ◽  
Cross Sectional ◽  
Mental Health Conditions

Child Marriage (before the age of 18) affects over 12 million young women globally, annually. Despite acknowledgement of the negative impacts of the practice on reproductive health, mental health consequences are largely overlooked. Given the ability for poor mental health to intensify other health and social challenges, understanding the mental health consequences linked to child marriage is vital. Our study is the first to examine how mental health is approached in current literature on child marriage. Our conceptual framework was informed by a rapid assessment of key issues in the field. Systematic searches of papers published between 2000-2020 were completed on four electronic databases with no language restrictions. Our protocol was registered on Prospero (CRD42019139685). Articles were assessed using PRISMA guidelines, and their quality assessed using the Joanna Briggs Institute Critical Appraisal Tools. Of the 4,457 records identified, 21 papers meeting inclusion criteria were analysed using narrative synthesis. The final sample included 5 qualitative, 1 mixed-methods and 15 quantitative studies (14 cross-sectional and 1 longitudinal study) reporting on data from 12 countries, largely in the global south. Intimate partner violence, poverty, challenges in childbirth and isolation were identified as social factors linked to emotional distress by those married as children. Depression was the most reported mental disorder. Anxiety, phobias, psychological distress, substance misuse, negative well-being and anti-social personality disorder were reported less frequently. Findings highlight that while significant emotional distress and specific mental health conditions are linked to child marriage, gaps in our understanding remain. Future studies are needed to; clarify directionality in these relationships; understand the mental health needs of young men, LGBTQI communities and those in humanitarian settings. Given the well documented cyclical relationship between social determinants and mental health conditions, we outline a series of community-oriented interventions which blend psychological, social and structural support to promote mental health and wellbeing in the contexts of child marriage.

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