Bibliographic record
Abstract
In Canada, there has been an increasing presence of and interest in natural health products (NHPs).While some of these products are deeply immersed in cultural heritage and have been used for decades, questions have emerged regarding the effectiveness, safety, and quality of these and other non-traditional NHPs.Some NHPs can cause adverse reactions when taken with other NHPs, prescription drugs, and even food; while others have been adulterated with contaminants.At the same time, Canadians are interested in health and wellness and are taking an active role in choosing products that may have a direct impact on their health.The Canadian government responded on January 1, 2004 by legislating the Natural Health Products Regulations (NHPR), whose main goal is to maintain the safety, efficacy, and quality of NHPs while allowing consumers to make an informed choice.The NHPR govern the sale, manufacture, packaging, labeling, importation, distribution and storage of NHPs; and provide regulations concerning licensing, good manufacturing practices, clinical trials, adverse reactions and health claims.The introduction of the NHPR is an innovative approach to regulating NHPs, but numerous challenges have emerged since its promulgation: processing backlogs of product licenses; classification and/or clarification of NHPs in food formats, of naturally-sourced prescription drugs, and of human-use antiseptic drugs; and knowledge dissemination of the safety hazards associated with non-licensed NHPs and previously licensed NHPs.This review outlines the regulation of NHPs in Canada and highlights some of the challenges and subsequent measures implemented to handle these issues.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".