bluuebunny/crossref_metadata_2025_split huggingface dataset:
Building Research Capacity and Organizational Empathy Among Students: Making Connections Beyond the Data
Authors: Durksen Anita , Struck Shannon , Guemili Alexandra , Han Soomin , Brownell Emily , Mahar Alyson , Nickel Nathan , Fransoo Randy , Brownell Marni , Enns Jennifer , Turnbull Lorna 
Published: December 2020

IntroductionThe leveraging of multi-sector, whole-population, linked administrative data is advantageous for conducting research on complex real-world problems. However, such large and complex data repositories can sometimes appear impersonal and overwhelming. Establishing organizational empathy (OE) in thecontext of a multi-sector partnership between academic, government and community representatives can help us understand the data better for social policy research. Evidence stemming from this research can then inform policy decisions, ultimately increasing the potency of linked data analysis and creating more meaningful student experiences. Our objective is to examine the role of OE in the student research experience.
Objectives and ApproachSPECTRUM (Social Policy Evaluation Collaborative Team Research at Universities in Manitoba) is a multi-disciplinary partnership working to provide evidence-based solutions to ‘wicked’ social issues by using linked data from multiple sectors. SPECTRUM provides fellowships to students to become partners in the collaboration. Students have participated in quarterly workshops, building relationships with community leaders, government decision-makers and academic researchers. Students are from various faculties, bringing their unique frameworks and research interests to the collective. Through OE, students observeand participate in SPECTRUM, relating its goals and outcomes to society and their own research.
ResultsStudent inclusion in SPECTRUM enhances the partnership by providing a greater range of perspectives and facilitates the development of OE among SPECTRUM members. Students are using linked administrative data, while actively engaging in dialogue with stakeholders, thereby enriching their knowledge and understanding of research.
Conclusion / ImplicationsData linkage involves more than just use of the repository; it requires establishing common ground since the data have different meaning to each partner. OE developed through SPECTRUM provides invaluable insight into and context for the data. Knowledge transfer among members of the partnership will enrich SPECTRUM’s research outcomes while building capacity among Students.
Healthcare Use for Violent Injury After Intimate Partner Violence Identified Through the Justice System: A Data Linkage Study
Authors: Urquia Marcelo L , Nesca Marcelo , Walld Randy , Au Wendy , Turnbull Lorna , Brownell Marni , Brownridge Douglas 
Published: December 2020

IntroductionRoutine health care information systems only capture a portion of violence against women because some victimized women may not seek health care and some events may not require medical attention. Population-based estimates of the risk of violent injury (VI) among women with a history of intimate partner victimization (IPV) are lacking.
Objectives and ApproachTo determine the risk of violent injury following IPV among women living in Manitoba, Canada, 2004-2016. Linked administrative justice, healthcare, and social databases were used. Exposure began after a woman was first involved with the Manitoba Justice system as a victim of IPV, assessed through provincial prosecution and disposition records. IPV victims (n= 20,469) were matched to three non-victims (n= 61,407) on age, relationship status and place of residence at the date of the IPV incident. The main outcomes were first health care use for violent injury and violent death. Outcomes were assessed through emergency department, hospital and vital statistics records. Conditional Cox Regression was used to obtain Hazard Ratios with 95% confidence intervals (CI).
ResultsThe crude risk of VI was 8.5 per 1000 women among non-victims and 55.8 among victims of IPV. Compared to non-victims, IPV victims were 3.8 [95% confidence interval (CI): 3.4, 4.3] times more likely to suffer IIIO and 4.5 [95% CI: 2.3, 9.0] times to have a violent death, after adjustment. Victims had approximately half the risk of VI if the accused is on probation.
Conclusion / ImplicationsJustice System-identified victims of IPV are at higher risk of assault and violent death than women not exposed to IPV. Justice involvement represents an opportunity for prevention of violent injury and homicide among IPV victims.
A National Approach to The Management of Multi-Jurisdictional Linked Data Projects in Australia
Authors: Wray Natalie , Flack Felicity , Irvine Katie 
Published: December 2020

IntroductionThe Population Health Research Network (PHRN) is an Australian data linkage infrastructure capable of securely and safely linking and integrating data collections from a wide range of sources. Australia, as a federated nation, has taken a national approach because:

Australia’s population is mobile and there is significant cross border service use
Commonwealth, state and territory government agencies collect and store their own data.

The national data linkage infrastructure enables researchers to apply for access to routinely collected and linked administrative data from the Commonwealth, six states and two territories. The application and approvals has been described by researchers as a time-consuming and administratively burdensome process.
Objectives and ApproachThe primary objective of this project was to improve coordination of the application and approval processes for researchers requesting linked data from multiple jurisdictions in Australia. The approach taken was to develop a single application form/online application system (OAS) and standardise the business processes for multi-jurisdictional research applications.
ResultsThe PHRN OAS was launched in 2014. All jurisdictions agreed to use the system for multi-jurisdictional linked data projects and SA NT DataLink agreed to use the system for their single-jurisdictional projects.
Each year since the PHRN OAS’s launch, both the total number of registered users and the number of submissions each year has increased. The majority oforganisations that lodge submissions are universities and medical research institutes.
There has been a significant reduction in the time taken by data linkage units to issue quotes and the time taken to achieve all approvals for multi-jurisdictional projects since the introduction of the standardised business processes.
Conclusion / ImplicationsThis paper demonstrates that despite the challenges and complexities associated with applying for linked data from multiple jurisdictions a coordinated application and approval approach can be achieved.
Improving Inter-Agency Data Sharing Through Linkage Spine Interoperability
Authors: Von Sanden Nick 
Published: December 2020

IntroductionLinkage of Federal Government data in Australia is conducted primarily through Accredited Integrating Authorities (AIAs). These agencies hold different dataset from Commonwealth and state/territory government agencies. Historically, linkage projects involving data held by different AIAs has been inefficient, requiring the transfer of identifiable data between agencies, and relinking data that have already been linked by another agency.
Objectives and ApproachTwo AIAs (the AIHW and ABS) have developed a system of interoperable linkage spines to address this issue. By using common datasets as a base, the agencies have improved the efficiency and security of linkage projects.
This process was developed through an analysis of spine datasets, and two test projects to share data between the agencies.
ResultsThe two test projects were successfully able to link cross-portfolio and cross-jurisdictional data without the need to share additional identifying information between the AIAs. Preliminary results suggest a high linkage rate from this process, and work is underway to quantify the linkage quality compared to traditional linkage methodologies. The ABS and AIHW are also investigating the implications for linkage quality as more datasets are included in the agencies’ linkage spines.
Conclusion / ImplicationsThe success of this project will increase the efficiency of cross-jurisdictional and cross-portfolio linkage in Australia. It will also allow specialised AIAs to work on datasets where they have specific expertise, and feed these into broader projects. This is expected to have an additional impact on public trust in the linkage system, by minimising the sharing of personally identifiable information while still maintaining high quality linkage.
Supporting Drug Regulators Through Simple Rapid Cycle Analyses
Authors: Camacho Ximena , Wilson Margaret , Paterson Michael , Pearson Sallie , Henry David 
Published: December 2020

IntroductionDrug regulators require timely, relevant information to address questions about the safety of prescribed medicines. Some questions can be informed by initial rapid and simple analyses of linked exposure/outcome data. These analyses will establish how many individuals have received the drug, their characteristics, the availability of follow up time and the frequency of the event of interest in the exposed cohort. This approach does not enable causal inferences but establishes the need for, and feasibility of, more complex controlled analyses. Discovery that the exposure or outcome are uncommon can allay initial fears about the extent of a problem.
Objectives and ApproachA pilot network to support the Australian drug regulator (TGA, Department of Health) was established among three research institutions in Australia and Canada. Initial queries were identified and prioritised by staff at the TGA. Academic staff at the partner institutions performed a rapid feasibility assessment based on existing knowledge and expertise. Following this, initial exposure/outcome analyses were undertaken on selected queries using routinely collected data from Canada and Australia. Updates were provided to the TGA at each stage to ensure that decision-makers were fully informed and participated in decision making.
ResultsTo date, the network has assessed 20 queries. Seven were deemed infeasible and 4 queries were being addressed by studies that were already planned or underway; in these cases, arrangements were made to provide early results directly to the regulator. The final four queries progressed to an initial exposure/outcome analysis, and one such study was expanded to a fully adjusted controlled analysis.
Conclusion / ImplicationsThis framework has proven to be agile and responsive and has strengthened the relationship between academia and the TGA. It may serve as a model for others who wish to engage more closely with governments or other decision makers.
Pre-Existing Mental Disorder, Clinical Profile, Inpatient Services, And Costs in People Hospitalised Following Traumatic Spinal Injury: A Population Record-Linkage Study
Authors: Sharwood Lisa Nicole , Wiseman Taneal , Tseris Emma , Curtis Kate , Vaikuntam Bharat , Craig Ashley , Young Jesse T 
Published: December 2020

IntroductionRisk of traumatic injury is increased in individuals with mental illness, substance use disorder and dual diagnosis (mental disorders); these conditions will pre-exist among individuals hospitalised with acute traumatic spinal injury (TSI). Although early intervention can improve outcomes for people who experience mental disorders or TSI, the incidence, management, and cost of this often complex comorbid health profile is not sufficiently understood.
Objectives and ApproachIn a whole-population cohort of patients hospitalised with acute TSI, we aimed to describe the prevalence of pre-existing mental disorders, and compare differences in injury epidemiology, costs and inpatient allied health service access.
Record-linkage study of all hospitalised cases of TSI between June 2013 and June 2016 in New South Wales, Australia. TSI was defined by specific ICD-10-AM codes. Mental disorder status was considered as pre-existing where specific ICD-10-AM codes were recorded in incident admissions.
Results13,489 individuals sustained acute TSI during this study. 13.11%, 6.06%, and 1.82% had pre-existing mental illness, substance use disorder, and dual diagnosis, respectively. Individuals with mental disorder were older (p<0.001), more likely to have had a fall or self-harmed (p<0.001), experienced almost twice the length of stay and inpatient complications, and increased injury severity compared to individuals without mental disorder (p<0.001).
Conclusion / ImplicationsIndividuals hospitalised for TSI with pre-existing mental disorder have greater likelihood of increased injury severity and more complex, costly acute care admissions compared to individuals without mental disorder. Care pathway optimisation including prevention of hospital acquired complications for people with pre-existing mental disorders hospitalised for TSI is warranted.
Early Life Predictors of Suspensions from Primary School: Cross-Agency Indicators of Risk for School Exclusion in An NSW Population Cohort
Authors: Laurens Kristin 
Published: December 2020

IntroductionExcluding students from school via out-of-school suspensions and expulsions is a controversial practice in Australia and internationally, yet forms a key component of school welfare and discipline policies. There is growing concern that increasing exclusion at earlier grades and an over-representation of particular groups of disadvantaged students may intensify inequalities in educational, justice, health and welfare outcomes for excluded students relative to their non-excluded peers. Previous studies have focused predominantly on secondary school exclusions and on sociodemographic predictors.
Objectives and ApproachThis study aimed to identify early life predictors of primary school suspensions using multi-agency data from the New South Wales (NSW) Child Development Study. Participants were 34,855 NSW children and their parents with linked records from education, health, child protection, and criminal justice spanning birth to the 6 th - grade (~12 years). Multinormal logistic regression examined the associations of sociodemographic, pre/perinatal, early developmental vulnerability, and parent factors with any later suspension during the 3 rd - to 6 th -grades.
ResultsIn a multivariable model, many factors had significant independent associations with any suspension. The largest effects (odds ratios ≥4.0) were observed for male gender and child maltreatment reports, with moderate effects (odds ratios 1.5 to <4.0) for Indigenous ethnicity, socio-economic disadvantage, exposure to smoking in utero, any parental criminal offending, aggressive behaviour, school-based responsibility and respect, and sub-threshold notifications to child protection services. Small effects (odds ratios >1.0 to <1.5) were revealed for young maternal age at birth (<26 years), any parental mental illness, language and cognitive skills, any emotional or behavioural problem, hyperactivity-inattention, and any emergency department presentation for physical injury.
Conclusion / ImplicationsBy facilitating the identification of students at risk of early (primary school) exclusion using multi-sector information available at the time of entry to school, these findings may assist educators to deliver targeted preventative interventions for at-risk students
Symposium: Distributed Networks of Federated Secure Research Data Environments – Enabling Analytics Across Multiple Platforms
Authors: Schneider Matthias 
Published: December 2020

IntroductionUsers of linked data require access to an increasing number of heterogeneous datasets from diverse domains, often held in different secure research data environments, especially for multi-jurisdictional projects. Under the traditional model of data access, projects are required to transfer and harmonise the necessary datasets in one central location before analysis can be undertaken, increasing the time required for data acquisition and preparation.
Objectives and ApproachIn a federated data environment, analysts query distributed datasets held in a network of multiple secure data environments via a central virtual database, without requiring the data to move. Instead, the data is analysed as close as possible to its storage location, minimising the amount of data transfers and giving data custodians more control over their data.
This symposium explores the challenges and opportunities of establishing and operating a distributed network of federated secure research data environments. Leading organisations operating data platforms in various jurisdictions present for 15 minutes each the current capabilities of their platforms, the landscape of data environments in their jurisdictions and potential approaches to key questions such as:

Harmonising/federating data sources
Data security
Data governance
Discoverability/metadata
Performance

The audience is the then invited to participate in discussing the topic for the remaining 30 minutes.
The following individuals have been approached to represent their organisations in this symposium:

Professor David Ford, Swansea University: UK Secure eResearch Platform (UK SErP)
Charles Victor, Institute for Clinical Evaluative Sciences (ICES): ICES Data & Analytic Virtual Environment (IDAVE)
Professor Louisa Jorm, Centre for Big Data Research in Health, University of New South Wales: E-Research Institutional Cloud Architecture (ERICA)
Professor Kimberlyn McGrail, Population Data BC: Secure Research Environment (SRE)

Results / Conclusion / ImplicationsThis symposium will help formulate requirements for and barriers to distributed networks of federated secure research data environments, and create a foundation for data analytics across multiple platforms.
Self-Reported Well-Being Indicators and Case Identification of Common Mental Health Disorders in Routinely Collected Health Data
Authors: Thompson Daniel , John Ann , Fry Richard , Watkins Alan 
Published: December 2020

IntroductionCommon mental health disorders (CMD) are significant contributors to impaired health and well-being, and drive greater health resource utilisation. Electronic health records (EHR) are increasingly used for case identification of CMD when ascertaining social determinants of mental health. We seek to compare self-reported well-being indicators in groups identified using EHR-based CMD methods.
Objectives and ApproachThe National Survey for Wales (NSW) contains self-reported well-being indicators (Warwick Edinburgh Mental Well-being Scale, WEMWBS) recorded annually on ~7,000 individuals. We combined data from two NSWs and linked well-being indicators with Welsh Longitudinal General Practice (WLGP) data within the Secure Anonymised Information Linkage (SAIL) Databank, using individual response dates. We then used WGLP data to algorithmically derive identifiers of CMD cases within survey respondents. This individual-level linkage enables a comparison of NSW responses in CMD and non-CMD cases, and to assess sensitivity and specificity of the current CMD algorithm.
ResultsSurvey participants comprised 18,450 adults aged 16+ and living in Wales during 16/17 or 18/19. WEMWBS responses indicate 2,338 (12.6%) participants could be considered possibly depressed, and 2,268 (12.3%) probably depressed with low mental well-being (LMW). For participants with LMW, a 42/58 percentage split is observed between male/female respondents, compared to a 45/55 respective split of those not identified with LMW. Participants with LMW recorded low measures for overall satisfaction with life, 998 (44%) reported a value of 5 or less (/10) compared to 1123 (7%) participants not identified with LMW. Similarly, 828 (37%) participants identified with LMW reported 5 or less (/10) on the life worthwhile index, compared to 800 (5%) of non-LMW participants.
Conclusion / ImplicationsLinkage to the NSW provides a rich data source to compare objective well-being to algorithmically derived CMD cases from routinely collected primary care data. The individual-level linkage involved will allow for the wider determinants of mental health disorders to be examined.
Markup: A Web-Based Clinical Annotation Tool with Enhanced Ontology Mapping
Authors: Dobbie Samuel , Strafford Huw , Pickrell W Owen , Fonferko-Shadrach Beata , Akbari Ashley , Thompson Simon , Lacey Arron 
Published: December 2020

IntroductionUnstructured free-text clinical notes often contain valuable information relating to patient symptoms, prescriptions and diagnoses. These can assist with better care for patients and novel healthcare research if transformed into accessible, structured clinical text. In particular, Natural Language Processing (NLP) algorithms can produce such structured outputs, but require gold standard data to train and validate their accuracy. While existing tools such as Brat and Webanno provide interfaces to manually annotate text, there is a lack of capability to efficiently annotate complex clinical information.
Objectives and ApproachWe present Markup, an open-source, web-based annotation tool developed for use within clinical contexts by domain experts to produce gold standard annotations for NLP development. Markup incorporates NLP and Active Learning technologies to enable rapid and accurate annotation of unstructured documents. Markup supports custom user configurations, automated annotation suggestions, and automated mapping to existing clinical ontologies such as the Unified Medical Language System (UMLS), the Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT), or custom, user-defined ontologies.
ResultsMarkup has been tested on Epilepsy clinic letters, where captured annotations were used to build and test NLP applications. Markup allowed for inter-annotator statistics to be calculated in the case of multiple annotators. Re-annotation, following iterations of annotation definitions, was incorporated for flexibility. UMLS codes, certainty context, and multiple components from complex phrases were all able to be captured and exported in a structured format.
Conclusions / ImplicationsMarkup allows gold standard annotations to be collected efficiently across unstructured text and is optimized to capture health-specific information. These annotations are important to develop and validate NLP algorithms that automate the capture of important information from clinic letters at scale.