Toward Evidence-based, Client-centred Nutrition Education Guidelines: Dietitian and Consumer Survey Results
Bibliographic record
Abstract
PURPOSE: Dietitian and consumer perspectives on nutrition education needs and preferences were explored, as these relate to health status METHODS: Phases 1 and 2 of a three-phase, mixed-methods study are reported. Phase 1 was a national online survey of dietitians, which was designed to inform the development of a consumer survey (Phase 2). Consumers responded to an online survey about their demographics, medical conditions, and nutrition education needs (what they wanted to learn) and preferences (how they wanted to learn). Phase 3 involved teleconferenced discussion groups with dietitians across Canada to develop guidelines for nutrition education. RESULTS: Dietitian respondents (n=441) perceived that consumer health status was important in predicting needs and preferences for nutrition education; emotional support was considered most important for consumers with life-altering medical conditions. Consumers (n=680) expressed interest in an array of nutrition education approaches; cooking tips, recipes, and supplement advice were the most popular. Respondents with and without medical conditions had similar nutrition education needs and preferences. CONCLUSIONS: Because of the complexity of nutrition education and consumers' preference for a spectrum of approaches and delivery methods, evidence-based nutrition education guidelines are important to inform dietetics training for the provision of client-centred nutrition education.
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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 | 0.029 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".