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Record W4386196152 · doi:10.1016/j.jneb.2023.07.008

Nutrition Knowledge Is Associated With Diet Quality Among US Army Soldiers

2023· article· en· W4386196152 on OpenAlexvenueno aff
Kenneth A. Sheafer, Dustin M. Lee, Beatriz George, Julianna M. Jayne, Renee E. Cole

Bibliographic record

VenueJournal of Nutrition Education and Behavior · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsnot available
FundersU.S. Army Medical Research and Development CommandGovernment of South AustraliaU.S. Department of Defense
KeywordsOdds ratioMedicineConfidence intervalOddsNational Health and Nutrition Examination SurveyActive dutyPsychological interventionEnvironmental healthGerontologyLogistic regressionDemographyMilitary personnelInternal medicinePsychiatryPopulation

Abstract

fetched live from OpenAlex

OBJECTIVE: Examine the relationship between nutrition knowledge, diet quality, and eating behavior among active-duty US Army Soldiers. METHODS: Cross-sectional study with data collection in February 2018 via paper surveys during the validation of the Military Eating Behavior Survey. RESULTS: Among 440 US Army Soldiers, nutrition knowledge was positively and significantly associated with diet quality (b = 0.29, P < 0.001). For every 1-point increase in nutrition knowledge, the Healthy Eating Index-2015 score was expected to increase by 0.29 points. Nutrition knowledge was not significantly associated with skipping breakfast (odds ratio, 1.01; 95% confidence interval, 0.98-1.04) or dining out (odds ratio, 1.01; 95% confidence interval, 0.98-1.03). CONCLUSIONS AND IMPLICATIONS: The outcomes of this study warrant further investigation to determine what interventions provide the strongest outcomes for improving nutrition knowledge and diet quality, as well as create and support an environment that enhances healthy behaviors regarding nutrition that lead to improved diet quality among active-duty Soldiers.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.095
GPT teacher head0.486
Teacher spread0.390 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations12
Published2023
Admission routes1
Has abstractyes

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