The Effects of an Anti-inflammatory Dietary Consultation on Self-efficacy, Adherence and Selected Health Outcomes: A Randomized Control Trial
Bibliographic record
Abstract
Research has shown that an anti-inflammatory diet can reduce inflammation and improve health outcomes in individuals with neurological disability, however, long term adherence is challenging. This study aimed to determine the effects of a 2-part dietary consultation, targeted at identified barriers for adherence in this population, on self-efficacy for adhering to an anti-inflammatory diet, as well as adherence and health outcomes one-month post-intervention. Eleven individuals (10 female, age 51.5±12.6 years) with neurological disability (7 multiple sclerosis, 3 spinal cord injury, 1 muscular dystrophy; 20.5 ± 10.6 years post-injury/diagnosis) participated. The intervention group (n = 7) received recipes for an anti-inflammatory diet and the consultation, while controls (n = 4) received the recipes only. The consultation included a home-visit involving cooking and accessible kitchen equipment demonstrations, and an accompanied trip to the grocery store. Task and barrier self-efficacy improved immediately following the consultation with trends for improvement one-month post-intervention. The consultation was also associated with increased dietary adherence one-month post-intervention and decreased depressive symptoms. Changes in dietary adherence (r = −.61; P = .045), and barrier self-efficacy (r = −.77; P = .009) were negatively correlated to changes in depression. Thus, a consultation targeted at barriers related to anti-inflammatory eating can improve self-efficacy for adherence as well as actual adherence and depressive symptomology one-month later.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".