Prediction of adherence to a gluten‐free diet using protection motivation theory among adults with coeliac disease
Bibliographic record
Abstract
BACKGROUND: Coeliac disease is a chronic autoimmune disease that requires strict adherence to a gluten-free diet. However, strict adherence to a gluten-free diet is difficult, with findings from a recent review suggesting that up to 42% of individuals with coeliac disease do not eat a strict gluten-free diet. METHODS: The present study aimed to examine psychosocial predictors of adherence (purposeful and accidental) to a gluten-free diet among adults with coeliac disease over a 1-month period. In this longitudinal study, 212 North American adults with coeliac disease completed online questionnaires at two time points, baseline and 1 month later. RESULTS: The results revealed that intentions partially mediated the effects of symptom severity, self-regulatory efficacy, planning and knowledge on purposeful gluten consumption. Intentions did not mediate the effects of severity, response cost, self-regulatory efficacy, planning and knowledge for accidental gluten consumption but, interestingly, self-regulatory efficacy directly predicted fewer accidental incidents of gluten-consumption. CONCLUSIONS: These findings delineate the differential psychological processes in understanding accidental and purposeful gluten consumption among adults with coeliac disease and emphasise the importance of bolstering self-regulatory efficacy beliefs to prevent accidental and purposeful consumption of gluten.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".