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Record W3012145872 · doi:10.1080/07448481.2020.1727911

Does health literacy affect fruit and vegetable consumption? An assessment of the relationship between health literacy and dietary practices among college students

2020· article· en· W3012145872 on OpenAlexaff
Alison Oberne, Cheryl A. Vamos, Lauri Wright, Wei Wang, Ellen M. Daley

Bibliographic record

VenueJournal of American College Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsHealth literacyHealth promotionSituational ethicsAffect (linguistics)Consumption (sociology)LiteracyCollege healthHealth educationEnvironmental healthPsychologyMedicinePublic healthGerontologyHealth careFamily medicineNursingPedagogySocial psychologySociologyCommunication

Abstract

fetched live from OpenAlex

Objective: To explore the association between health literacy and fruit and vegetable (F&V) consumption among college students. Participants: In 2018, undergraduate students from a large, southeastern university were recruited to participate in this study. Methods: Participants (n = 436) completed an online survey assessing health literacy, F&V intake, and personal, situational, and societal and environmental determinants of health literacy. Results: There was a significant association between general health literacy, F(2, 161.54) = 6.52, p < .001; disease prevention health literacy, F(2, 214.22) = 4.788, p < .01; and health promotion health literacy, F(2, 138.35) = 5.53, p < .01 with F&V consumption. Students with excellent health literacy consumed significantly more fruits and vegetables than students with limited health literacy. Conclusions: Health literacy may play an important role in F&V consumption among college students. Future research should explore the relationship between the health literacy and dietary practice decision-making to inform intervention development among college students.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.110
GPT teacher head0.538
Teacher spread0.428 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations26
Published2020
Admission routes1
Has abstractyes

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