<i>Canadian Dietitians’ Attitudes Toward</i> Functional Foods and Nutraceuticals
Bibliographic record
Abstract
PURPOSE: A telephone survey was conducted to determine dietitians' views on nutraceuticals and functional foods. METHODS: Using systematic sampling with a random start, 238 names were drawn from the Dietitians of Canada membership. A survey instrument containing mostly open-ended questions and two pages of definitions was pretested and revised. Accurate description was used to analyze and summarize the data with a minimum of interpretation. RESULTS: Of 180 dietitians contacted, 151 (84%) completed interviews. The majority (n=91, 60%) of respondents thought health claims should be permitted on foods, but only with adequate scientific support for claims and government regulation. Participants overwhelmingly (n=122, 81%) felt that dietitians were the most appropriate professionals to recommend functional foods, but held mixed views of the appropriateness of having dietitians recommend nutraceuticals. However, according to a rating scale of 0 to 10, respondents across all areas of practice believed that it is extremely important for dietitians to become knowledgeable about nutraceuticals (mean +/- standard deviation [SD] = 9.0 +/- 1.2) and functional foods (mean +/- SD = 9.5 +/- 0.9). CONCLUSIONS: Dietitians recommended strict legislation and close monitoring by government; unbiased scientific studies with consensus that the findings support health claims; partnerships with other health professionals, especially pharmacists; and opportunities to gain further knowledge.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".