Community Identification of Natural Health Product–Drug Interactions
Bibliographic record
Abstract
BACKGROUND: The majority of Canadians use natural health products (NHPs), most of which are purchased in pharmacies. Community pharmacists regularly field inquiries regarding NHPs. As such, pharmacists are ideally placed to answer questions about NHP use and interactions with other medications. OBJECTIVE: To identify community pharmacists' familiarity with NHPs and NHP-related adverse events (AEs) and their knowledge and ability to counsel on potential and known NHP-drug interactions. METHODS: Survey questions were derived from a literature review of previous surveys, data collected from Health Canada, and in consultation with clinicians, pharmacists, policy-makers, and researchers. A convenience sample of 321 community pharmacists in Alberta and British Columbia were asked to participate. RESULTS: We received responses from 132 pharmacists, resulting in a response rate of 41% (132/321). A total of 19% of the sample had previously reported an adverse event to Health Canada. When asked specifically about NHP-drug interactions/AEs, 47% of pharmacists stated that they had identified a potential interaction; however, only 2 of these reported it to Health Canada. Pharmacists were most familiar (76% of respondents) with the interaction between sertraline and St. John's wort and were least familiar with interactions between NHPs and anti-retrovirals. CONCLUSIONS: This survey provides evidence to suggest that pharmacists encounter reportable NHP-drug interactions, yet rarely choose to report these events. The current lack of available data on NHP AEs makes it difficult to provide patients and healthcare providers with useful strategies for managing AEs and drug interactions. Changes to the current system of monitoring AEs due to NHPs and further education of healthcare professionals regarding NHP-drug interactions is required.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".