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Establishing Criterion Validity for the Revised Critical Nutrition Literacy Tool in U.S. College Students

Overview
Specialty Medical Education
Date 2021 Mar 10
PMID 33688546
Citations 4
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Abstract

Background: Critical nutrition literacy (CNL) plays an important role in how college students make everyday decisions about nutrition choices. Increasing CNL is an aim of many introductory nutrition courses, but there are limited instruments measuring this construct. This study aimed to assess the changeability of CNL and the relationship between CNL and markers of diet quality in young adults.

Design: This was a two-phase research project consisting of a nonexperimental, pre-post study and a cross-sectional assessment from 2018 to 2019. Participants were U.S. college students, 18-24 years old, recruited from introductory-level courses from three participating universities, located in Rhode Island, West Virginia, and New Jersey.

Subjects And Methods: Interventions consisted of (1) a 4-credit, 13-week nutrition course and (2) a cross-sectional, online behavior, environment, and perception survey. CNL was measured using the Revised CNL Tool (CNLT-R) instrument across both phases. Measures for phases include: (1) the changeability of CNL and (2) the relationship between CNL and markers of diet quality.

Analysis: Paired -tests and multivariate analysis of variance were utilized through SPSS version 25.0.

Results: CNL score significantly increased from baseline to postintervention from 3.38 ± 0.48 to 3.61 ± 0.55 ( = 0.014). There was an overall significant effect of CNL on markers of diet quality, such as cups of fruits and vegetables (F/V) and teaspoons of added sugar (F [2,1321] = 3.12, < 0.05; Wilks' Λ = 0.99).

Conclusions: This research found that an introduction to nutrition course was associated with an increase in CNL and that CNL is related to diet quality. The instrument could be used by nutrition educators as an outcome assessment. Future research should investigate other components of the CNL construct as well as predictive validity.

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