Student Competition (Clinical/Best Practice Implementation) ID 1983939
Bibliographic record
Abstract
Background Previous research has shown that an anti-inflammatory diet can reduce inflammation and improve health outcomes in individuals with neurological disability; however, long term dietary adherence has proven to be challenging. Accordingly, we have designed a 2-part consultation targeted at identified barriers for adherence to an anti-inflammatory diet in this population. Objectives This study aimed to determine the effects of the consultation on self-efficacy for adhering to an anti-inflammatory diet, as well as adherence and health outcomes one month post-intervention. Design/Methods 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 2-part consultation, while controls (n=4) received the recipes only. The consultation consisted of a home-visit that included cooking and accessible kitchen equipment demonstrations, and an accompanied trip to the grocery store. Results Both task and barrier self-efficacy improved immediately following the consultation and tended to stay above baseline one month post-intervention. The consultation was also associated with increased dietary adherence one month post-intervention and decreased depressive symptoms as measured by the Centre for Epidemiological Studies Depression Scale (CES-D). Changes in dietary adherence (r=-0.61; p=0.045), and barrier self-efficacy (r=-0.77; p=0.009) were negatively correlated to changes in CES-D scores. Conclusions 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. Further follow-up studies to determine the persistence of these effects are warranted.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.813 | 0.550 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".