Context:The process of knowledge translation (KT) in health research depends on the activities of a wide range of actors, including health professionals, researchers, the public, policymakers, and research funders. Little is known, however, about health research funding agencies' support and promotion of KT. Our team asked thirty-three agencies from Australia, Canada, France, the Netherlands, Scandinavia, the United Kingdom, and the United States about their role in promoting the results of the research they fund.Methods: Semistructured interviews were conducted with a sample of key informants from applied health funding agencies identified by the investigators. The interviews were supplemented with information from the agencies' websites. The final coding was derived from an iterative thematic analysis. engagement in this process. The agencies' abilities to create a pull for research findings; to engage in linkage and exchange between agencies, researchers, and decision makers; and to push results to various audiences differed as well. Finally, the evaluation of the effectiveness of KT strategies remains a methodological challenge. Conclusions:Funding agencies need to think about both their conceptual framework and their operational definition of KT, so that it is clear what is and what is not considered to be KT, and adjust their funding opportunities and activities accordingly. While we have cataloged the range of knowledge translation activities conducted across these agencies, little is known about their effectiveness and so a greater emphasis on evaluation is needed. It would appear that "best practice" for funding agencies is an elusive concept depending on the particular agency's size, context, mandate, financial considerations, and governance structure.

BackgroundVast 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.ObjectiveIn this paper, we explore whether text mining can automatically identify mental health disorders from this unstructured text.MethodsWe 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.ResultsThe 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).ConclusionsThe 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.

ObjectiveTo summarise the extent and quality of evidence on the association between prison cell spatial density (a measure of crowding) and infectious and communicable diseases transmission among prisoners.DesignSystematic review.Data sourcesEmbase, PubMed, Medline, Scopus, Web of Science, PsycINFO, PsycExtra, ProQuest Databases, ProQuest Dissertations and Theses Global, Index to Legal Periodicals, InformitOnline, Cochrane Library, Criminal Justice Abstracts and ICONDA were searched to 31 December 2018.Eligibility criteriaStudies that reported on the association between prison cell spatial density (measured in square feet or square metres of cell floor area per person) and infectious and communicable diseases in juvenile and adult populations incarcerated in a correctional facility.Data extraction and synthesisA review protocol was developed in consultation with an advisory panel. Two reviewers independently extracted data and used the Australian National Health and Medical Research Council’s (NHMRC) checklist to critically appraise individual studies. An assessment of the overall body of the evidence was conducted using the NHMRC’s Evidence Scale and Statement Form.ResultsA total of 5126 articles were initially identified with seven included in the review from Pakistan (2003), Chile (2016), Nigeria (2012, 2013) and the USA (1980s). Infectious and communicable disease outcomes included pneumococcal disease/acute pneumonia,Mycobacterium tuberculosis, latent tuberculosis infection, infectious skin conditions and contagious disease reporting to the prison clinic. Five articles reported statistically significant positive associations but were countered by associations possibly being explained by chance, bias or confounding factors. Heterogeneity prevented meta-analysis.ConclusionOverall, the body of evidence provides some support for an association between prison cell special density and infectious and communicable diseases, but care should be taken in the interpretation and transferability of the findings. Future research and policy responses should adequately consider prospective mediating factors implicated in associations between cell spatial density and health effects.

BackgroundThe 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.ObjectiveWe 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.MethodsWe 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.ResultsTesting 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.ConclusionsThe 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.

Most respondents (80%) "strongly agreed" or "agreed" that EBP would improve the effectiveness of their efforts in a disadvantaged region. However, more than half of respondents (56%) "strongly agreed" or "agreed" that there is lack of evidence for interventions in population health. Eighty two per cent of respondents "strongly agreed" or "agreed" that training in EBP is important for all population health workers. Those who used evidence also needed a greater capacity to discriminate "good" from "bad" research (85% in agreement). Contradictory policy was cited by one third of respondents as acting against EBP.