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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Design Recommendations for Self-Monitoring in the Workplace: Studies in Software Development
Meyer, A. N., Murphy, G. C., Zimmermann, T., & Fritz, T. (2017) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3134714 · Cited by 60
Meloncon, L., & Warner, E. (2017) · 2017 IEEE International Professional Communication Conference (ProComm) · doi:10.1109/ipcc.2017.8013960 · Cited by 50
O’Grady, L., Witteman, H., Bender, J. L., Urowitz, S., Wiljer, D., & Jadad, A. R. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1058 · Cited by 43
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
