MétaCan
Menu
Back to cohort
Record W2603770231 · doi:10.47339/ephj.2014.156

Non-celiac consumer knowledge regarding gluten-free diets

2014· article· en· W2603770231 on OpenAlexfundvenueno aff
Andrew Hou, Environmental Health BCIT School of Health Sciences, Helen Heacock

Bibliographic record

VenueBCIT Environmental Public Health Journal · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsnot available
FundersBritish Columbia Institute of Technology
KeywordsGluten freeGlutenPopulationTest (biology)MedicineEnvironmental healthBiology

Abstract

fetched live from OpenAlex


 BACKGROUND: With the rising trend in gluten-free diets, it is imperative that there is high consumer product literacy so that the public makes informed decisions in regards to their diet and health. Knowledge taken from reputable sources and recognizing unsubstantiated health claims regarding gluten-free diets is critical for a non-celiac consumer. METHODS: A survey was used to investigate why non-celiac consumers elect to follow gluten-free diets and why they believe that the elimination of gluten from their diet is healthy. This project also tested consumer knowledge regarding gluten. RESULTS: During a 2 month period, total of 376 individuals participated in the survey. Only 322 participants fell under the inclusion criteria of this study. Women who elected to participate in gluten-free diets (but did not have Celiac’s Disease themselves) had higher overall test scores and men in the general population had lower overall test scores (p = 0.000017). CONCLUSIONS: Based on overall test scores and percentages of correct responses for specific questions, there seems to be deficiencies in both the average consumer and non-celiac-gluten-avoider-consumer knowledge regarding gluten, gluten-free products and diets.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.257
Teacher spread0.241 · 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 designNot applicable
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

Citations0
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueBCIT Environmental Public Health JournalSame topicNutrition, Genetics, and DiseaseFrench-language works237,207