Translating person-specific network models into personalized treatments: Development and demonstration of the dynamic assessment treatment algorithm for individual networks (DATA-IN)
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The identification of which active elements work for whom could improve outcomes via development of personalized treatment protocols, where individuals receive a specific suite of active elements suited to their needs.
“The identification of which active elements work for whom could improve outcomes via develop ment of personalized treatment protocols, where individuals receive a specific suite of active elements suited to their needs (Fisher et al., 2019; Rubel, Fisher, Husen, & Lutz, 2018).”
From Measuring the active elements of cognitive-behavioral therapies (2023) · cites this paper as Rubel, Fisher, Husen, & Lutz, 2018
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- Authors
- Rubel, J. A., Fisher, A. J., Husen, K., & Lutz, W.
- Affiliations
- Universität Trier (DE)
- University of California, Berkeley (US)
- Venue
- Psychotherapy and Psychosomatics
- Publisher
- S. Karger AG
- Published
- 2018
- Volume
- 87
- Issue
- 4
- Pages
- 249-251
- ISSN
- 0033-3190 (ISSN-L)
- 1423-0348
- OpenAlex
- W2801604488
- Cited by
- 86
- References
- 9
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 15 · 2023: 9 · 2022: 13 · 2021: 12 · 2020: 10 · 2019: 12 · 2018: 1
- FWCI
- 11.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychotherapy and Psychosomatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational and Psychological Assessments
- Keywords
- network models, personalized treatment, treatment algorithm, psychotherapy
- MeSH terms
- Algorithms, Humans, Psychotherapy, Models, Statistical, Cognitive Behavioral Therapy, Precision Medicine
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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