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Development of a Core Set of the International Classification of Functioning, Disability and Health (ICF) for Geriatric Patients in Primary Care

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University of Erlangen-Nürnberg Medical School · 2017

Abstract

The sum of medical interventions can do more harm than good. Supporting patients' resources instead of combating disease might effectively help reducing "too much medicine". A first step to approach this goal would be to have a classification tool that captures the essential features of a meaningful human life, namely body functions, activities, and participation. The International Classification of Functioning, Disability and Health (ICF) is the perfect framework to distinguish between necessary and unnecessary medical interventions by offering descriptions of outcomes of medical and non-medical interventions. However, though adequate and comprehensive in theory, it is impracticable in a primary care setting. Therefore, a core set of the ICF for the old and very old in primary care is needed. Four preparatory studies according to the international development guideline will be carried out: a systematic literature review to capture the research perspective, a qualitative study to assess the patients' perspective, an experts' survey and an empirical multicenter study to detect the clinical perspective.

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ClinicalTrials.gov
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2026-10-24 MST

Cite this

APA
Anonymous. (2017). Development of a Core Set of the International Classification of Functioning, Disability and Health (ICF) for Geriatric Patients in Primary Care. <em>University of Erlangen-Nürnberg Medical School</em>. https://clinicaltrials.gov/study/NCT03384732
Vancouver
Anonymous. Development of a Core Set of the International Classification of Functioning, Disability and Health (ICF) for Geriatric Patients in Primary Care. University of Erlangen-Nürnberg Medical School. 2017.
BibTeX
@misc{anon2017Develo, title = {Development of a Core Set of the International Classification of Functioning, Disability and Health (ICF) for Geriatric Patients in Primary Care}, author = {Anonymous}, journal = {University of Erlangen-Nürnberg Medical School}, year = {2017}, }

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