ORCID Profile
0000-0003-0795-5363
Current Organisations
University of Queensland
,
University of Manchester
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Artificial Intelligence and Image Processing not elsewhere classified | Operations Research | Stochastic Analysis and Modelling | Artificial Intelligence and Image Processing
Logistics | Fisheries - Aquaculture not elsewhere classified | Sustainability Indicators |
Publisher: Elsevier BV
Date: 12-2015
Publisher: Elsevier BV
Date: 12-2015
Publisher: JMIR Publications Inc.
Date: 22-09-2023
DOI: 10.2196/49721
Publisher: JMIR Publications Inc.
Date: 05-12-2022
DOI: 10.2196/42891
Abstract: Epidemiological criminology refers to health issues affecting incarcerated and nonincarcerated offender populations, a group recognized as being challenging to conduct research with. Notwithstanding this, an urgent need exists for new knowledge and interventions to improve health, justice, and social outcomes for this marginalized population. To better understand research outputs in the field of epidemiological criminology, we examined the lead author’s affiliation by analyzing peer-reviewed published outputs to determine countries and organizations (eg, universities, governmental and nongovernmental organizations) responsible for peer-reviewed publications. We used a semiautomated approach to examine the first-author affiliations of 23,904 PubMed epidemiological studies related to incarcerated and offender populations published in English between 1946 and 2021. We also mapped research outputs to the World Justice Project Rule of Law Index to better understand whether there was a relationship between research outputs and the overall standard of a country’s justice system. Nordic countries (Sweden, Norway, Finland, and Denmark) had the highest research outputs proportional to their incarcerated population, followed by Australia. University-affiliated first authors comprised 73.3% of published articles, with the Karolinska Institute (Sweden) being the most published, followed by the University of New South Wales (Australia). Government-affiliated first authors were on 8.9% of published outputs, and prison-affiliated groups were on 1%. Countries with the lowest research outputs also had the lowest scores on the Rule of Law Index. This study provides important information on who is publishing research in the epidemiological criminology field. This has implications for promoting research ersity, independence, funding equity, and partnerships between universities and government departments that control access to incarcerated and offending populations.
Publisher: JMIR Publications Inc.
Date: 05-12-2018
Publisher: JMIR Publications Inc.
Date: 13-09-2018
DOI: 10.2196/11548
Publisher: JMIR Publications Inc.
Date: 24-08-2020
Abstract: he 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. he aim of this study is to present the prevalence of extracted mental illness mentions for POIs and victims in police-recorded DV events. e applied a knowledge-driven text mining method to recognize mental illness mentions for victims and POIs from police-recorded DV events. n 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%). 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.
Publisher: JMIR Publications Inc.
Date: 11-12-2018
Abstract: he police attend numerous domestic violence events each year, recording details of these events as both structured (coded) data and unstructured free-text narratives. Abuse types (including physical, psychological, emotional, and financial) conducted by persons of interest (POIs) along with any injuries sustained by victims are typically recorded in long descriptive narratives. e aimed to determine if an automated text mining method could identify abuse types and any injuries sustained by domestic violence victims in narratives contained in a large police dataset from the New South Wales Police Force. e used a training set of 200 recorded domestic violence events to design a knowledge-driven approach based on syntactical patterns in the text and then applied this approach to a large set of police reports. esting our approach on an evaluation set of 100 domestic violence events provided precision values of 90.2% and 85.0% for abuse type and victim injuries, respectively. In a set of 492,393 domestic violence reports, we found 71.32% (351,178) of events with mentions of the abuse type(s) and more than one-third (177,117 events 35.97%) contained victim injuries. “Emotional/verbal abuse” (33.46% 117,488) was the most common abuse type, followed by “punching” (86,322 events 24.58%) and “property damage” (22.27% 78,203 events). “Bruising” was the most common form of injury sustained (51,455 events 29.03%), with “cut/abrasion” (28.93% 51,284 events) and “red marks/signs” (23.71% 42,038 events) ranking second and third, respectively. he results suggest that text mining can automatically extract information from police-recorded domestic violence events that can support further public health research into domestic violence, such as examining the relationship of abuse types with victim injuries and of gender and abuse types with risk escalation for victims of domestic violence. Potential also exists for this extracted information to be linked to information on the mental health status.
Publisher: National Institute for Health and Care Research
Date: 06-2020
DOI: 10.3310/HSDR08280
Abstract: Collecting NHS patient experience data is critical to ensure the delivery of high-quality services. Data are obtained from multiple sources, including service-specific surveys and widely used generic surveys. There are concerns about the timeliness of feedback, that some groups of patients and carers do not give feedback and that free-text feedback may be useful but is difficult to analyse. To understand how to improve the collection and usefulness of patient experience data in services for people with long-term conditions using digital data capture and improved analysis of comments. The DEPEND study is a mixed-methods study with four parts: qualitative research to explore the perspectives of patients, carers and staff use of computer science text-analytics methods to analyse comments co-design of new tools to improve data collection and usefulness and implementation and process evaluation to assess use of the tools and any impacts. Services for people with severe mental illness and musculoskeletal conditions at four sites as exemplars to reflect both mental health and physical long-terms conditions: an acute trust (site A), a mental health trust (site B) and two general practices (sites C1 and C2). A total of 100 staff members with erse roles in patient experience management, clinical practice and information technology 59 patients and 21 carers participated in the qualitative research components. The tools comprised a digital survey completed using a tablet device (kiosk) or a pen and paper/online version guidance and information for patients, carers and staff text-mining programs reporting templates and a process for eliciting and recording verbal feedback in community mental health services. We found a lack of understanding and experience of the process of giving feedback. People wanted more meaningful and informal feedback to suit local contexts. Text mining enabled systematic analysis, although challenges remained, and qualitative analysis provided additional insights. All sites managed to collect feedback digitally however, there was a perceived need for additional resources, and engagement varied. Observation indicated that patients were apprehensive about using kiosks but often would participate with support. The process for collecting and recording verbal feedback in mental health services made sense to participants, but was not successfully adopted, with staff workload and technical problems often highlighted as barriers. Staff thought that new methods were insightful, but observation did not reveal changes in services during the testing period. The use of digital methods can produce some improvements in the collection and usefulness of feedback. Context and flexibility are important, and digital methods need to be complemented with alternative methods. Text mining can provide useful analysis for reporting on large data sets within large organisations, but qualitative analysis may be more useful for small data sets and in small organisations. New practices need time and support to be adopted and this study had limited resources and a limited testing time. Further research is needed to improve text-analysis methods for routine use in services and to evaluate the impact of methods (digital and non-digital) on service improvement in varied contexts and among erse patients and carers. This project was funded by the NIHR Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research Vol. 8, No. 28. See the NIHR Journals Library website for further project information.
Publisher: JMIR Publications Inc.
Date: 05-04-2019
DOI: 10.2196/13007
Publisher: JMIR Publications Inc.
Date: 06-06-2023
Abstract: n order to identify the health needs of incarcerated in iduals, research is needed for the development of policies and programs which can address health inequities. The emerging field of epidemiological criminology studies the intersection between the public health and justice systems focusing on prevalent health issues that affect offending and incarcerated populations. Following stakeholder consultation, a logical next step is to analyse the gap between research priorities and existing research via publications. xamine published research outputs in epidemiological criminology to assess gaps between published outputs and current research priorities identified by prison stakeholders. ext mining study. A rule-based method was applied to 23,904 PubMed epidemiological criminology abstracts to extract the study determinants and outcomes (i.e., “themes”). These were mapped against research priorities identified by Australian prison stakeholders to assess differences from research outputs. The income level for the affiliation country of the first authors was also identified to compare the ranking of research priorities in income country groups. n an evaluation set of 100 abstracts, the identification of themes returned an F1-Score of 90.0% indicating reliable performance. More than 50.0% of articles had at least one extracted theme the most common was substance use (12.9%) followed by the Human Immunodeficiency Virus (12.6%). Infectious diseases (24.9%) was the most common research priority category, followed by mental health (24.0%) and alcohol and other drug use (20.5%). A comparison between the extracted themes and the stakeholder priorities showed an alignment for mental health, infectious diseases and alcohol and other drug use. While behaviour and juvenile related themes were common, they did not feature as prison priorities. Most research derived was from high income countries (85.3%) while countries with the lowest income status focused half of their research on infectious diseases (51.6%). dentification of research themes from PubMed epidemiological criminology research abstracts is possible through the application of a rule-based text mining method. The frequency of investigated themes may reflect historical developments concerning disease prevalence, treatment advances, and social understandings of illness and incarcerated populations. Differences between income status groups are likely to be explained by local health priorities and immediate health risks. Notable gaps between stakeholder research priorities and research outputs concerned themes more focused on social factors and systems and may reflect publication bias or self-publication-selection highlighting the need for further research on prison health services and social determinants of health. Different jurisdictions, countries, and regions should undertake similar systematic and transparent research priority-setting processes. >
Publisher: ACM
Date: 21-10-2023
Publisher: JMIR Publications Inc.
Date: 12-03-2019
DOI: 10.2196/13067
Publisher: Institute of Computational Linguistics, University of Zurich
Date: 2012
DOI: 10.5167/UZH-64476
Publisher: JMIR Publications Inc.
Date: 20-10-2022
DOI: 10.2196/39373
Abstract: To better understand domestic violence, data sources from multiple sectors such as police, justice, health, and welfare are needed. Linking police data to data collections from other agencies could provide unique insights and promote an all-of-government response to domestic violence. The New South Wales Police Force attends domestic violence events and records information in the form of both structured data and a free-text narrative, with the latter shown to be a rich source of information on the mental health status of persons of interest (POIs) and victims, abuse types, and sustained injuries. This study aims to examine the concordance (ie, matching) between mental illness mentions extracted from the police’s event narratives and mental health diagnoses from hospital and emergency department records. We applied a rule-based text mining method on 416,441 domestic violence police event narratives between December 2005 and January 2016 to identify mental illness mentions for POIs and victims. Using different window periods (1, 3, 6, and 12 months) before and after a domestic violence event, we linked the extracted mental illness mentions of victims and POIs to clinical records from the Emergency Department Data Collection and the Admitted Patient Data Collection in New South Wales, Australia using a unique identifier for each in idual in the same cohort. Using a 2-year window period (ie, 12 months before and after the domestic violence event), less than 1% (3020/416,441, 0.73%) of events had a mental illness mention and also a corresponding hospital record. About 16% of domestic violence events for both POIs (382/2395, 15.95%) and victims (101/631, 16.01%) had an agreement between hospital records and police narrative mentions of mental illness. A total of 51,025/416,441 (12.25%) events for POIs and 14,802/416,441 (3.55%) events for victims had mental illness mentions in their narratives but no hospital record. Only 841 events for POIs and 919 events for victims had a documented hospital record within 48 hours of the domestic violence event. Our findings suggest that current surveillance systems used to report on domestic violence may be enhanced by accessing rich information (ie, mental illness) contained in police text narratives, made available for both POIs and victims through the application of text mining. Additional insights can be gained by linkage to other health and welfare data collections.
Publisher: JMIR Publications Inc.
Date: 24-12-2020
DOI: 10.2196/23725
Abstract: 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. The aim of this study is to present the prevalence of extracted mental illness mentions for POIs and victims in police-recorded DV events. We applied a knowledge-driven text mining method to recognize mental illness mentions for victims and POIs from police-recorded DV events. 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%). 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.
Publisher: JMIR Publications Inc.
Date: 05-04-2017
DOI: 10.2196/JMIR.6173
Publisher: JMIR Publications Inc.
Date: 12-07-2018
Abstract: ast 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. n this paper, we explore whether text mining can automatically identify mental health disorders from this unstructured text. e 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. he 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). he 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.
Publisher: Elsevier BV
Date: 11-2017
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 05-2021
End Date: 12-2024
Amount: $365,000.00
Funder: Australian Research Council
View Funded Activity