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Record W4400800468 · doi:10.1016/j.jneb.2024.06.005

Assessment of Interest in a Virtual Avatar-Based Nutrition Education Program Among Youth-Serving Community Partners

2024· article· en· W4400800468 on OpenAlexvenueno aff
Basheerah Enahora, Gina L. Tripicchio, Régis Kopper, Omari L. Dyson, Jeffrey D. Labban, Lenka H. Shriver, Lauren A. Haldeman, Christopher K. Rhea, Jared T. McGuirt

Bibliographic record

VenueJournal of Nutrition Education and Behavior · 2024
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
FundersOffice of Planning, Research and EvaluationAdministration for Children and FamiliesU.S. Department of Health and Human Services
KeywordsAvatarAppealOddsLogistic regressionPsychologyMedical educationApplied psychologyMedicineComputer sciencePolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: Examine the appeal of a virtual avatar-led nutrition education program among youth-serving community partners in North Carolina. METHODS: We surveyed community partners using the Diffusion of Innovation Theory constructs of relative advantage, compatibility, and complexity. Logistic regression evaluated the appeal and likelihood of the program's future use. RESULTS: Community partners (n = 100) agreed that the program was an innovative (87%) and convenient (85%) way for youth and parents to learn about nutrition. Partners who perceived the program as a relative advantage to current programs had significantly higher odds of future use intention (P = 0.005). Those who found it compatible with organizational and personal values had significantly higher odds of future use (P < 0.001). CONCLUSIONS AND IMPLICATIONS: A nutrition education virtual avatar program is of interest to youth-engaged community partners. Future research examining the potential integration of this type of program within community organizations is warranted.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.194
GPT teacher head0.519
Teacher spread0.325 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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