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Record W4385351293 · doi:10.1016/j.jneb.2023.06.001

Determining the Effectiveness of an Adult Food Literacy Program Using a Matched Control Group

2023· article· en· W4385351293 on OpenAlexvenueno aff
Andrea Begley, Isabelle Fisher, Lucy M. Butcher, Frances Foulkes‐Taylor, Roslyn Giglia, Satvinder S. Dhaliwal

Bibliographic record

VenueJournal of Nutrition Education and Behavior · 2023
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersDepartment of Health, Government of Western AustraliaCurtin University of TechnologyFederal Security Agency
KeywordsLiteracySocioeconomic statusEnvironmental healthDisadvantageFood groupPsychologyFood choiceFood preparationGerontologyMedicineDemographyFood processingFood scienceComputer scienceBiologyPedagogyPopulation

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the effect of Food Sensations for Adults on food literacy behaviors and selected dietary behaviors. METHODS: A quasi-experimental design using preprogram and postprogram questionnaires over 4 weeks with a control group (n = 75) matched for sex, age group, and socioeconomic disadvantage to program participants (n = 867). General linear mixed models assessed change in food literacy behavior frequency in 3 self-reported domains (plan and manage, selection, and preparation) and fruit and vegetable servings. RESULTS: Postprogram, Food Sensations for Adults participants reported modest yet statistically significant score improvements in 2 of the 3 domains of food literacy behaviors in the plan and manage (12.4%) and preparation (9.8%) domains, as well as servings of vegetables (22.6% or 0.5 servings). CONCLUSION AND IMPLICATIONS: Quasi-experimental designs indicate food literacy programs can produce modest short-term changes across a range of food literacy and dietary behaviors.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.357
Teacher spread0.336 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Nutrition Education and Behavior→Same topicObesity, Physical Activity, Diet→French-language works237,207→