{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T19:29:44Z","timestamp":1765826984385},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2011,8,6]],"date-time":"2011-08-06T00:00:00Z","timestamp":1312588800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2011,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Low health literacy has a detrimental effect on health outcomes, as well as ability to use online health resources. Good health literacy assessment tools must be brief to be adopted in practice; test development from the perspective of item-response theory requires pretesting on large participant populations. Our objective was to develop a novel classification method for developing brief assessment instruments that does not require pretesting on large numbers of research participants, and that would be suitable for computerized adaptive testing.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>We present a new algorithm that uses principles of measurement decision theory (MDT) and Shannon's information theory. As a demonstration, we applied it to a secondary analysis of data sets from two assessment tests: a study that measured patients' familiarity with health terms (52 participants, 60 items) and a study that assessed health numeracy (165 participants, 8 items).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>In the familiarity data set, the method correctly classified 88.5% of the subjects, and the average length of test was reduced by about 50%. In the numeracy data set, for a two-class classification scheme, 96.9% of the subjects were correctly classified with a more modest reduction in test length of 35.7%; a three-class scheme correctly classified 93.8% with a 17.7% reduction in test length.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>MDT-based approaches are a promising alternative to approaches based on item-response theory, and are well-suited for computerized adaptive testing in the health domain.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1472-6947-11-52","type":"journal-article","created":{"date-parts":[[2011,8,6]],"date-time":"2011-08-06T14:15:46Z","timestamp":1312640146000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A new adaptive testing algorithm for shortening health literacy assessments"],"prefix":"10.1186","volume":"11","author":[{"given":"Sasikiran","family":"Kandula","sequence":"first","affiliation":[]},{"given":"Jessica S","family":"Ancker","sequence":"additional","affiliation":[]},{"given":"David R","family":"Kaufman","sequence":"additional","affiliation":[]},{"given":"Leanne M","family":"Currie","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Zeng-Treitler","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2011,8,6]]},"reference":[{"key":"429_CR1","doi-asserted-by":"crossref","DOI":"10.17226\/10883","volume-title":"Book Health Literacy: A Prescription to End Confusion (Editor ed.^eds.)","author":"L Nielsen-Bohlman","year":"2004","unstructured":"Nielsen-Bohlman L, Panzer AM, Kindig DA, Committee on Health Literacy: Health Literacy: A Prescription to End Confusion. 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