scholarly journals Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study (Preprint)

2018 ◽  
Author(s):  
George Karystianis ◽  
Armita Adily ◽  
Peter Schofield ◽  
Lee Knight ◽  
Clara Galdon ◽  
...  
Keyword(s):  
Mental Health ◽  
Domestic Violence ◽  
Text Mining ◽  
Large Scale ◽  
New South ◽  
New South Wales ◽  
Mental Health Disorders ◽  
Free Text ◽  
South Wales ◽  
Health Disorders

BACKGROUND Vast numbers of domestic violence (DV) incidents are attended by the New South Wales Police Force each year in New South Wales and recorded as both structured quantitative data and unstructured free text in the WebCOPS (Web-based interface for the Computerised Operational Policing System) database regarding the details of the incident, the victim, and person of interest (POI). Although the structured data are used for reporting purposes, the free text remains untapped for DV reporting and surveillance purposes. OBJECTIVE In this paper, we explore whether text mining can automatically identify mental health disorders from this unstructured text. METHODS We used a training set of 200 DV recorded events to design a knowledge-driven approach based on lexical patterns in text suggesting mental health disorders for POIs and victims. RESULTS The precision returned from an evaluation set of 100 DV events was 97.5% and 87.1% for mental health disorders related to POIs and victims, respectively. After applying our approach to a large-scale corpus of almost a half million DV events, we identified 77,995 events (15.83%) that mentioned mental health disorders, with 76.96% (60,032/77,995) of those linked to POIs versus 16.47% (12,852/77,995) for the victims and 6.55% (5111/77,995) for both. Depression was the most common mental health disorder mentioned in both victims (22.30%, 3258) and POIs (18.73%, 8918), followed by alcohol abuse for POIs (12.24%, 5829) and various anxiety disorders (eg, panic disorder, generalized anxiety disorder) for victims (11.43%, 1671). CONCLUSIONS The results suggest that text mining can automatically extract targeted information from police-recorded DV events to support further public health research into the nexus between mental health disorders and DV.

scholarly journals Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study (Preprint)

2020 ◽  
Author(s):  
George Karystianis ◽  
Annabeth Simpson ◽  
Armita Adily ◽  
Peter Schofield ◽  
David Greenberg ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Mental Illness ◽  
Text Mining ◽  
New South ◽  
Mental Illnesses ◽  
Free Text ◽  
South Wales ◽  
Police Records ◽  
Health Justice ◽  
Improve Health

BACKGROUND The New South Wales Police Force (NSWPF) records details of significant numbers of domestic violence (DV) events they attend each year as both structured quantitative data and unstructured free text. Accessing information contained in the free text such as the victim’s and persons of interest (POI's) mental health status could be useful in the better management of DV events attended by the police and thus improve health, justice, and social outcomes. OBJECTIVE The aim of this study is to present the prevalence of extracted mental illness mentions for POIs and victims in police-recorded DV events. METHODS We applied a knowledge-driven text mining method to recognize mental illness mentions for victims and POIs from police-recorded DV events. RESULTS In 416,441 police-recorded DV events with single POIs and single victims, we identified 64,587 events (15.51%) with at least one mental illness mention versus 4295 (1.03%) recorded in the structured fixed fields. Two-thirds (67,582/85,880, 78.69%) of mental illnesses were associated with POIs versus 21.30% (18,298/85,880) with victims; depression was the most common condition in both victims (2822/12,589, 22.42%) and POIs (7496/39,269, 19.01%). Mental illnesses were most common among POIs aged 0-14 years (623/1612, 38.65%) and in victims aged over 65 years (1227/22,873, 5.36%). CONCLUSIONS A wealth of mental illness information exists within police-recorded DV events that can be extracted using text mining. The results showed mood-related illnesses were the most common in both victims and POIs. Further investigation is required to determine the reliability of the mental illness mentions against sources of diagnostic information.

scholarly journals Correction: Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study

10.2196/13007 ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. e13007 ◽  
Author(s):  
George Karystianis ◽  
Armita Adily ◽  
Peter Schofield ◽  
Lee Knight ◽  
Clara Galdon ◽  
...  
Keyword(s):  
Mental Health ◽  
Domestic Violence ◽  
Text Mining ◽  
Mental Health Disorders ◽  
Automatic Extraction ◽  
Health Disorders

Domestic violence events involving autism: a text mining study of police records in New South Wales, 2005-2016

2020 ◽  
Vol 78 ◽  
pp. 101634
Author(s):  
Ye In Hwang ◽  
Lidan Zheng ◽  
George Karystianis ◽  
Vicki Gibbs ◽  
Kym Sharp ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Text Mining ◽  
New South ◽  
New South Wales ◽  
South Wales ◽  
Police Records

scholarly journals Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study

10.2196/23725 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e23725
Author(s):  
George Karystianis ◽  
Annabeth Simpson ◽  
Armita Adily ◽  
Peter Schofield ◽  
David Greenberg ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Mental Illness ◽  
Text Mining ◽  
New South ◽  
Mental Illnesses ◽  
Free Text ◽  
South Wales ◽  
Police Records ◽  
Health Justice ◽  
Improve Health

Background The New South Wales Police Force (NSWPF) records details of significant numbers of domestic violence (DV) events they attend each year as both structured quantitative data and unstructured free text. Accessing information contained in the free text such as the victim’s and persons of interest (POI's) mental health status could be useful in the better management of DV events attended by the police and thus improve health, justice, and social outcomes. Objective The aim of this study is to present the prevalence of extracted mental illness mentions for POIs and victims in police-recorded DV events. Methods We applied a knowledge-driven text mining method to recognize mental illness mentions for victims and POIs from police-recorded DV events. Results In 416,441 police-recorded DV events with single POIs and single victims, we identified 64,587 events (15.51%) with at least one mental illness mention versus 4295 (1.03%) recorded in the structured fixed fields. Two-thirds (67,582/85,880, 78.69%) of mental illnesses were associated with POIs versus 21.30% (18,298/85,880) with victims; depression was the most common condition in both victims (2822/12,589, 22.42%) and POIs (7496/39,269, 19.01%). Mental illnesses were most common among POIs aged 0-14 years (623/1612, 38.65%) and in victims aged over 65 years (1227/22,873, 5.36%). Conclusions A wealth of mental illness information exists within police-recorded DV events that can be extracted using text mining. The results showed mood-related illnesses were the most common in both victims and POIs. Further investigation is required to determine the reliability of the mental illness mentions against sources of diagnostic information.

scholarly journals Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study

10.2196/11548 ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. e11548 ◽  
Author(s):  
George Karystianis ◽  
Armita Adily ◽  
Peter Schofield ◽  
Lee Knight ◽  
Clara Galdon ◽  
...  
Keyword(s):  
Mental Health ◽  
Domestic Violence ◽  
Text Mining ◽  
Mental Health Disorders ◽  
Automatic Extraction ◽  
Health Disorders

Mental health data: A case for the African communities in New South Wales

2021 ◽  
Vol 42 (1) ◽  
pp. 64-80
Author(s):  
Daniel Kwai Apat ◽  
Wellington Digwa
Keyword(s):  
Mental Health ◽  
Mental Health Services ◽  
Mental Disorders ◽  
Health Services ◽  
New South ◽  
Mental Health Policy ◽  
New South Wales ◽  
Community Members ◽  
South Wales ◽  
Health Related

This paper examines mental health policies in relation to African communities residing in New South Wales, Australia and the attitudes of African communities toward mental disorders and mental health services. Current mental health policy frameworks have shown an inadequate inclusion of African communities. This may negatively affect the design of mental health interventions and how African communities engage with mental health services. The available mental health literature on African communities showed disjointed and uncoordinated data which focuses on specific community-groups within African communities. Insufficient mental health or suicide data, combined with African community members’ perception toward mental disorders and mental health services, makes it very difficult to progress engagement and interventions. There is a need for proper and sizable data on mental health related to people of African descent in NSW and Australia wide, if positive outcomes are to be realised.

scholarly journals Mental health disorders among students from rural areas three months after returning to school: a cross-sectional study among Polish students (12-13 y.o.)

2022 ◽  
Author(s):  
Piotr Długosz
Keyword(s):  
Mental Health ◽  
Rural Areas ◽  
Large Scale ◽  
Negative Impact ◽  
Positive Impact ◽  
High Stress ◽  
Mental Health Disorders ◽  
Mental Condition ◽  
Health Disorders ◽  
Returning To School

Abstract: Background: All over the world, the negative impact of the Covid-19 pandemic on children and adolescents’ mental health is observed. The conducted research aims to verify whether returning to schools, to the education inside the classroom in the company of their peers, improved or undermined the students’ mental health. Metods: The study was carried out on a sample of students inhabiting rural areas in a borderland region. The research sample was collected using purposive sampling and consisted of 552 respondents from 7th and 8th grades of primary school. An auditorium questionnaire was used to gather the research material. Results: Three months after returning to school, the students are in a bad mental condition. 61% of the respondents are satisfied with their lives, 52% of the respondents show symptoms of depression measured with the WHO-5 index, whereas 85% of them have average and high stress levels as measured with the PSSC scale. Higher levels of mental disorders was observed among females, the students inhabiting villages and evaluating their financial status as worse. Conclusions: Returning to schools failed to have a positive impact on the students’ mental health. Disorders occurring at a large scale will have a negative influence on the students’ performance and hinder their re-adaptation to school. Educational authorities shall immediately provide the students with support and monitor the situation in the next months.

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