Natural Health Products: Practices, Perceptions and Training Needs of Registered Dietitians
Bibliographic record
Abstract
Canadians have access to thousands of authorized natural health products (NHPs) and are also surrounded by many unauthorized NHPs, which may place them at risk of adverse effects. Consumers expect health professionals, including nutritionists, to be a source of information on NHPs. Current training programs suggest that registered dietitians may have little knowledge about NHPs. The Ordre professionnel des diététistes du Quebec (OPDQ) sent an electronic survey to registered dietitians who are members of the OPDQ to document their use, referral habits and sources of information for NHPs. The survey also explored respondents’ perceptions of professional roles regarding NHPs as well as their perceptions of the effectiveness of specific NHPs. It measured their need for training on specific NHPs and on the health conditions that may be improved by NHPs. Data were analyzed with SPSS, through which descriptive statistics were obtained. A qualitative analysis was performed on the open-ended questions from the survey. A total of 295 questionnaires were analyzed. Among nutritionists, 93% have received requests for information about NHPs, 91% use or have used NHPs and 94% have recommended them. Also, 95% need training on NHPs and for various health considerations. Overall, they have a positive perception of their roles regarding this subject, with 77% indicating that the nutritionist should be a reliable source of information for NHPs. The major findings of our study are that the roles of Quebec nutritionists relating to NHPs are not clearly defined. Nutritionists need training on NHPs to provide sound nutritional advice for NHP users and, therefore, should develop a new area of nutritional practice.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".