ArticleJournal of safety research2020
Advancing injury and violence prevention through data science.
Article in Journal of safety research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Lessons learned in a data linkage project on fatal drowning.Injury epidemiology · 2026Article
- Probabilistic data linkage to study the epidemiology of unintentional fatal drowning in a large metropolitan region.Injury epidemiology · 2025Article
- Advances in diagnostic codes to document sexual assault in health care service.Health services research · 2023Article
- Pathways to suicide or collections of vicious cycles? Understanding the complexity of suicide through causal mapping.Social network analysis and mining · 2022Article
- Leveraging data science to enhance suicide prevention research: a literature review.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2022Review
- Identifying nonfatal firearm assault incidents through linking police data and clinical records: Cohort study in Indianapolis, Indiana, 2007-2016.Preventive medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
introductionThe volume of new data that is created each year relevant to injury and violence prevention continues to grow. Furthermore, the variety and complexity of the types of useful data has also progressed beyond traditional, structured data. In order to more effectively advance injury research and prevention efforts, the adoption of data science tools, methods, and techniques, such as natural language processing and machine learning, by the field of injury and violence prevention is imperative.
methodThe Centers for Disease Control and Prevention's (CDC) National Center for Injury Prevention and Control has conducted numerous data science pilot projects and recently developed a Data Science Strategy. This strategy includes goals on expanding the availability of more timely data systems, improving rapid identification of health threats and responses, increasing access to accurate health information and preventing misinformation, improving data linkages, expanding data visualization efforts, and increasing efficiency of analytic and scientific processes for injury and violence, among others.
resultsTo achieve these goals, CDC is expanding its data science capacity in the areas of internal workforce, partnerships, and information technology infrastructure. Practical Application: These efforts will expand the use of data science approaches to improve how CDC and the field address ongoing injury and violence priorities and challenges.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.