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Record W2221393186 · doi:10.5539/jfr.v5n1p88

Impact of a Pilot Intervention to Improve Nutrition Knowledge and Cooking Confidence Among Low-Income Individuals

2015· article· en· W2221393186 on OpenAlexvenueno aff
Stacey C. Driver, Carol A. Friesen

Bibliographic record

VenueJournal of Food Research · 2015
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsNutrition EducationMedicineFood preparationIntervention (counseling)Low incomeSupplemental Nutrition Assistance ProgramEnvironmental healthConfidence intervalFood groupLow ConfidenceGerontologyFood safetyPsychologyNursingFood insecurityFood securityAgricultureSocial psychology

Abstract

fetched live from OpenAlex

Poor dietary behaviors, limited food preparation skills, and low levels of self-efficacy toward preparing healthy meals have been indicated in low-income and food insecure populations. The purpose of this pilot intervention was to determine the effectiveness of a community cooking demonstration at increasing participants’ general nutrition knowledge and confidence to prepare healthy meals with limited resources. Data was analyzed from a convenience sample of 23 low-income adults associated with Head Start (n=8) or a local soup kitchen (n=15) in the Midwestern United States. Participants attended a one-hour presentation comprised of a cooking demonstration, taste testing, and basic education on the MyPlate food guide and food safety. Subjects completed a pre- and post-assessment to measure changes in cooking confidence and general food and nutrition knowledge. Results indicated that, although there were no significant improvements in participants’ confidence to prepare healthy meals (39.3±11.3 vs. 44.5±9.1; t=1.76, p=0.25), subjects experienced significant gains in knowledge related to the MyPlate food guide (1.2±0.5 vs. 1.8±0.8; t=2.82, p=0.01) and basic food safety (0.7±0.9 vs. 2.5±1.0; t=6.05, p<0.01). Further research is necessary to identify effective strategies for parlaying increased nutrition knowledge into improved self-efficacy and behavior change.

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)0.008
metaresearch head score (Gemma)0.002
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 score0.058
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.406
GPT teacher head0.590
Teacher spread0.184 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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