The FAIR database: facilitating access to public health research literature.

Autor:	Zhao Z; Department of Computer Science, University of Sheffield, Sheffield S10 2TN, United Kingdom., Thomas J; EPPI Centre, UCL Social Research Institute, Institute of Education, University College London, London WC1E 6BT, United Kingdom., Kell G; School of Medicine and Population Health, University of Sheffield, Sheffield S10 2TN, United Kingdom., Stansfield C; EPPI Centre, UCL Social Research Institute, Institute of Education, University College London, London WC1E 6BT, United Kingdom., Clowes M; Department of Population Health Sciences, School of Life Course & Population Sciences, Faculty of Life Sciences & Medicine, Kings College London, London WC2R 2LS, United Kingdom., Graziosi S; EPPI Centre, UCL Social Research Institute, Institute of Education, University College London, London WC1E 6BT, United Kingdom., Brunton J; EPPI Centre, UCL Social Research Institute, Institute of Education, University College London, London WC1E 6BT, United Kingdom., Marshall IJ; School of Medicine and Population Health, University of Sheffield, Sheffield S10 2TN, United Kingdom., Stevenson M; Department of Computer Science, University of Sheffield, Sheffield S10 2TN, United Kingdom.
Jazyk:	angličtina
Zdroj:	JAMIA open [JAMIA Open] 2024 Dec 13; Vol. 7 (4), pp. ooae139. Date of Electronic Publication: 2024 Dec 13 (Print Publication: 2024).
DOI:	10.1093/jamiaopen/ooae139
Abstrakt:	Objectives: In public health, access to research literature is critical to informing decision-making and to identify knowledge gaps. However, identifying relevant research is not a straightforward task since public health interventions are often complex, can have positive and negative impacts on health inequalities and are applied in diverse and rapidly evolving settings. We developed a "living" database of public health research literature to facilitate access to this information using Natural Language Processing tools. Materials and Methods: Classifiers were identified to identify the study design (eg, cohort study or clinical trial) and relationship to factors that may be relevant to inequalities using the PROGRESS-Plus classification scheme. Training data were obtained from existing MEDLINE labels and from a set of systematic reviews in which studies were annotated with PROGRESS-Plus categories. Results: Evaluation of the classifiers showed that the study type classifier achieved average precision and recall of 0.803 and 0.930, respectively. The PROGRESS-Plus classification proved more challenging with average precision and recall of 0.608 and 0.534. The FAIR database uses information provided by these classifiers to facilitate access to inequality-related public health literature. Discussion: Previous work on automation of evidence synthesis has focused on clinical areas rather than public health, despite the need being arguably greater. Conclusion: The development of the FAIR database demonstrates that it is possible to create a publicly accessible and regularly updated database of public health research literature focused on inequalities. The database is freely available from https://eppi.ioe.ac.uk/eppi-vis/Fair. Netscc Id Number: NIHR133603. Competing Interests: None declared. (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)
Databáze:	MEDLINE
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