The ethics of dietary supplements and natural health products in pharmacy practice: A systematic documentary analysis
Bibliographic record
Abstract
OBJECTIVES: Many natural health products and dietary supplements are purchased in pharmacies and it has been argued that pharmacists are in the best position to provide patients with evidence-based information about them. This study was designed to identify how the pharmacist's role with respect to natural health products and dietary supplements is portrayed in the literature. METHOD: A systematic search was conducted in a variety of health databases to identify all literature that pertained to both pharmacy and natural health products and dietary supplements. Of the 786 articles identified, 665 were broad-coded and 259 were subjected to in-depth qualitative content analysis for emergent themes. KEY FINDINGS: Overwhelmingly, support for the sale of natural health products and dietary supplements in pharmacies is strong. Additionally, a role for pharmacist counselling is underscored. But another recurrent theme is that pharmacists are ill-equipped to counsel patients about these products that are available on their shelves. This situation has led some to question the ethics of pharmacists selling natural health products and dietary supplements and to highlight the existence of an ethical conflict stemming from the profit-motive associated with sales of natural health products and dietary supplements. CONCLUSIONS: This analysis raises concerns about the ethics of natural health products and dietary supplements being sold in pharmacies, and about pharmacists being expected to provide counselling about products of which they have little 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 | 0.069 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".