Understanding predictors of gluten-free dietary adherence and physical activity: An Organismic Integration Theory approach
Bibliographic record
Abstract
The objective of the present study was to examine behavioural regulations for gluten-free dietary adherence and PA consistent with Organismic Integration Theory (OIT; Deci & Ryan, 2002). Those currently consuming a gluten-free diet (N = 202; Mage = 42.35; SDage = 12.43) were asked to complete a series of online questionnaires on a single occasion. Overall, 72.3% of the sample adhered to a strict gluten-free diet across the previous 7 days. Participant PA scores were higher than normative values (p = .00; Godin & Shephard, 1985; Wilson et al., 2010). Behavioural regulations consistent with OIT to consume a gluten-free diet predicted 5% (?2 = .08) of adherence with integrated (s = -.32) and identified (s = .30) regulations emerging as significant predictors. Intrinsic and identified regulations (ss = .28) were found to be significant predictors of PA scores with the overall model accounting for 31% (?2 = .33) of the variance. Understanding predictors of gluten-free dietary adherence and PA are essential to the overall health for those living with a gluten-related disorder. These findings add to the existing literature, provide practical applications and offer insight into future directions for health-based researchers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".