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Can Treatment Adherence Be Improved by Using Rubin's Four Tendencies Framework to Understand a Patient's Response to Expectations
Open accessAbstract
Within the context of poorer patient outcomes and rising healthcare costs, we need to better understand why many patients do not engage fully with their treatment plan. Movement away from talking about “compliance” towards “adherence” and “concordance” is evidence of a recognition that this is a two-way process. Whilst healthcare professionals expect patients to engage in treatment, equally, patients have expectations (whether positive or negative) of their treatment and their need for engagement. There is a need for an effective method that can specifically target those interventions that will provide the most benefit to individual patients and which, crucially, is easy and inexpensive to administer in everyday practice and widely applicable. Rubin's Four Tendencies model identifies a patient's “response to outer and inner expectations” as a key factor in adherence. The model therefore provides an opportunity to test such a targeted, patient-specific strategy and we present a call to action for research in this area.
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Determinants of adherence to heart failure medication: a systematic literature review
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Adherence to Depression Treatment in Primary Care: A Randomized Clinical Trial
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Metadata
- Authors
- Kirk, J., MacDonald, A., Lavender, P., Dean, J., & Rubin, G.
- Venue
- Biomedicine Hub
- Publisher
- S. Karger AG
- Published
- 2017-08-29
- Volume
- 2
- Issue
- 2
- Pages
- 1-12
- ISSN
- 2296-6870 (ISSN-L)
- OpenAlex
- W2746562707
- Cited by
- 3,887
- References
- 30
- FWCI
- 349.85
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
- creativecommons.org/licenses/by-nc-nd/4.0/ (tdm)
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 ›
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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.
