The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Rethinking Health Numeracy: A Multidisciplinary Literature Review
Open accessAbstract
The purpose of this review is to organize various published conceptions of health numeracy and to discuss how health numeracy contributes to the productive use of quantitative information for health. We define health numeracy as the individual-level skills needed to understand and use quantitative health information, including basic computation skills, ability to use information in documents and non-text formats such as graphs, and ability to communicate orally. We also identify two other factors affecting whether a consumer can use quantitative health information: design of documents and other information artifacts, and health-care providers' communication skills. We draw upon the distributed cognition perspective to argue that essential ingredients for the productive use of quantitative health information include not only health numeracy but also good provider communication skills, as well as documents and devices that are designed to enhance comprehension and cognition.
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Citations
Cites
Health Literacy: A Prescription to End Confusion
Committee on Health Literacy, Board on Neuroscience and Behavioral Health, & Institute of Medicine (2004) · doi:10.17226/10883 · Cited by 3,444
Representations in Distributed Cognitive Tasks
Zhang, J., & Norman, D. A. (1994) · Cognitive Science · doi:10.1207/s15516709cog1801_3 · Cited by 1,203
Peters, E., Västfjäll, D., Slovic, P., Mertz, C. K., Mazzocco, K., & Dickert, S. (2006) · Psychological Science · doi:10.1111/j.1467-9280.2006.01720.x · Cited by 1,180
Redesigning a Telehealth Diabetes Management Program for a Digital Divide Seniors Population
Kaufman, D. R., Pevzner, J., Hilliman, C., Weinstock, R. S., Teresi, J., Shea, S., & Starren, J. (2006) · Home Health Care Management & Practice · doi:10.1177/1084822305281949 · Cited by 39
Self-citation
Cited by
Good intentions are not enough: how informatics interventions can worsen inequality
Veinot, T. C., Mitchell, H., & Ancker, J. S. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy052 · Cited by 704
Self-citationEnduring links from childhood mathematics and reading achievement to adult socioeconomic status
Ritchie, S. J., & Bates, T. C. (2013) · Psychological Science · doi:10.1177/0956797612466268 · Cited by 715
Garcia-Retamero, R., & Cokely, E. T. (2017) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720817690634 · Cited by 298
Unertl, K. M., Schaefbauer, C. L., Campbell, T. R., Senteio, C., Siek, K. A., Bakken, S., & Veinot, T. C. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv094 · Cited by 198
ChatGPT in medical school: how successful is AI in progress testing?
Friederichs, H., Friederichs, W. J., & März, M. (2023) · Medical Education Online · doi:10.1080/10872981.2023.2220920 · Cited by 122
Metadata
- Authors
- Ancker, J. S., & Kaufman, D.
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-11-01
- Volume
- 14
- Issue
- 6
- Pages
- 713-721
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- OpenAlex
- W2101812029
- Cited by
- 275
- References
- 4
- FWCI
- 31.73
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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