Non-celiac consumer knowledge regarding gluten-free diets
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".