uConsent: Addressing the gap in measuring understanding of informed consent in clinical research

Autor: Richard F. Ittenbach, J. William Gaynor, Jenny M. Dorich, Nancy B. Burnham, Guixia Huang, Madisen T. Harvey, Jeremy J. Corsmo
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Clinical and Translational Science, Vol 16, Iss 12, Pp 2530-2542 (2023)
Druh dokumentu: article
ISSN: 1752-8062
1752-8054
DOI: 10.1111/cts.13645
Popis: Abstract The purpose of this study was to establish the technical merit, feasibility, and generalizability of a new measure of understanding of informed consent for use with clinical research participants. A total of 109 teens/young adults at a large, pediatric medical center completed the consenting process of a hypothetical biobanking study. Data were analyzed using a combination of classical and modern theory analytic methods to produce a final set of 19 items referred to as the uConsent scale. A requirement of the scale was that each item mapped directly onto one or more of the Basic Elements of Informed Consent from the 2018 Final Rule. Descriptive statistics were computed for each item as well as the scale as a whole. Partial credit (Rasch) logistic modeling was then used to generate difficulty/endorsability estimates for each item. The final, 19‐item uConsent scale was derived using inferential methods to yield a set of items that ranged across difficulty levels (−3.02 to 3.10 logits) with a range of point‐measure correlations (0.12 to 0.50), within‐range item‐ and model‐fit statistics, varying item types mapped to both Bloom's Taxonomy of Learning and required regulatory components of the 2018 Final Rule. Median coverage rate for the uConsent scale was 95% for the 25 randomly selected studies from ClinicalTrials.gov. The uConsent scale may be used as an effective measure of informed consent when measuring and documenting participant understanding in clinical research studies today.
Databáze: Directory of Open Access Journals
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