ArticleJournal of the Medical Library Association : JMLA2024
Attitudes on data reuse among internal medicine residents.
Article in Journal of the Medical Library Association : JMLA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
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Abstract
Background: NYU Langone Health offers a collaborative research block for PGY3 Primary Care residents that employs a secondary data analysis methodology. As discussions of data reuse and secondary data analysis have grown in the data library literature, we sought to understand what attitudes internal medicine residents at a large urban academic medical center had around secondary data analysis. This case report describes a novel survey on resident attitudes around data sharing. Methods: We surveyed internal medicine residents in three tracks: Primary Care (PC), Categorical, and Clinician-Investigator (CI) tracks as part of a larger pilot study on implementation of a research block. All three tracks are in our institution's internal medicine program. In discussions with residency directors and the chief resident, the term "secondary data analysis" was chosen over "data reuse" due to this being more familiar to clinicians, but examples were given to define the concept. Results: We surveyed a population of 162 residents, and 67 residents responded, representing a 41.36% response rate. Strong majorities of residents exhibited positive views of secondary data analysis. Moreover, in our sample, those with exposure to secondary data analysis research opined that secondary data analysis takes less time and is less difficult to conduct compared to the other residents without curricular exposure to secondary analysis. Discussion: The survey reflects that residents believe secondary data analysis is worthwhile and this highlights opportunities for data librarians. As current residents matriculate into professional roles as clinicians, educators, and researchers, libraries have an opportunity to bolster support for data curation and education.
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