Dietitians’ Perspectives on Interventions to Enhance Adherence to Dietary Advice for Chronic Diseases in Adults
Bibliographic record
Abstract
PURPOSE: To assess dietitians' perspectives on the importance and applicability of interventions to enhance adherence to dietary advice for preventing and managing chronic diseases in adults in the Canadian context. METHODS: Based on a Cochrane systematic review, we identified 8 promising interventions for enhancing adherence to dietary advice: behavioural contracts, exchange lists, feedback based on self-monitoring, individualized menu suggestions, multiple interventions, portion size awareness, telephone follow-up, and videos. Thirty-two dietitians then completed a 3-round Delphi study by responding to an electronic questionnaire asking them to rate the importance and applicability in their practice of the 8 interventions on a 7-point Likert scale. RESULTS: Using a ≥75% level of agreement, 4 interventions showed strong consensus: multiple interventions, feedback based on self-monitoring, portion size awareness, and videos. Among these, the most significant were (means ± SD for importance and applicability, respectively) feedback based on self-monitoring (6.97 ± 0.18 and 6.72 ± 0.46), portion size awareness (6.69 ± 0.54 and 6.75 ± 0.51), and multiple interventions (6.94 ± 0.25 and 6.81 ± 0.40). CONCLUSIONS: These findings can guide the development of educational training sessions for dietitians to help them provide practice-relevant interventions that will increase the likelihood that patients adhere to their advice regarding prevention and management of chronic diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".