Motives for adherence to a gluten‐free diet: a qualitative investigation involving adults with coeliac disease
Bibliographic record
Abstract
BACKGROUND: Currently , the only treatment for coeliac disease is life long adherence to a strict gluten-free diet. Strict adherence to a gluten-free diet is challenging, with recent reports suggesting that adherence rates range from 42% to 91%. The present study aimed to: (i) identify motives for adhering to a gluten-free diet and (ii) explore factors implicated in adherence and non-adherence behaviour in terms of accidental and purposeful gluten consumption among adults with coeliac disease. METHODS: Two hundred and three adults with coeliac disease completed an online questionnaire. Using a qualitative design, relationships were examined between reported adherence and motivation to follow a gluten-free diet, as well as the onset, duration and severity of symptoms. RESULTS: Feelings of desperation (‘hitting rock bottom’) and needing to gain or lose weight were associated with the strictest adherence to a gluten-free diet. Participants who accidentally consumed gluten over the past week developed symptoms the most quickly and reported the most pain over the past 6 months. Participants who consumed gluten on purpose over the past week reported a shorter duration of symptoms and less pain over the past 6 months. CONCLUSIONS: Hitting rock bottom and needing to gain or lose weight were factors associated with the strictest adherence, when considered in the context of both accidental and purposeful gluten consumption. Future research is warranted to develop resources to help people with coeliac disease follow a strict gluten-free diet.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".