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Record W3166559914 · doi:10.3390/nu13062044

Examining the Correlates of Adolescent Food and Nutrition Knowledge

2021· article· en· W3166559914 on OpenAlexafffundabout
Rachel Brown, Jamie A. Seabrook, Saverio Stranges, Andrew Clark, Jess Haines, Colleen O’Connor, Sean Doherty, Jason Gilliland

Bibliographic record

VenueNutrients · 2021
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsWilfrid Laurier UniversityUniversity of GuelphChildren’s Health Research InstituteLawson Health Research InstituteWestern University
FundersCanadian Institutes of Health ResearchChildren's Health FoundationHeart and Stroke Foundation of Canada
KeywordsPsychological interventionCurriculumMetropolitan areaEnvironmental healthIntervention (counseling)PopulationNutrition EducationHealth literacyPublic healthLiteracyGerontologyPsychologyMedicineHealth carePolitical sciencePedagogyNursing

Abstract

fetched live from OpenAlex

Food literacy is a set of skills and knowledge that are integral to diet. It is common among teenagers to not have basic food literacy skills needed to consume a healthy diet. This study examined: (1) the current state of food and nutrition knowledge among adolescents 13-19 years of age in the census metropolitan area of London, ON, Canada; and (2) correlates of food knowledge and nutrition knowledge among adolescents. Data for this study were drawn from baseline youth and parent survey data collected from a larger population health intervention study. Statistical analysis of the survey data indicates that higher parental education and higher median neighbourhood family income, the use of mobile health applications, liking to cook, as well as confidence in reading and understanding food labels were all consistently associated with increased food and nutrition knowledge. Findings may help guide future research towards optimal methods for delivering food literacy interventions to effectively educate teenagers. Results of this study may help guide policy makers, researchers, and public health professionals in developing appropriate food and nutrition programs and curriculums to combat the decline in food literacy skills.

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)0.000
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 score0.047
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.031
GPT teacher head0.271
Teacher spread0.240 · 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

Citations58
Published2021
Admission routes3
Has abstractyes

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