Reporting natural health product related adverse drug reactions: is it the pharmacist's responsibility?
Bibliographic record
Abstract
OBJECTIVES: Herbal medicines and other natural health products (NHPs) are sold in Canadian pharmacies as over-the-counter products, yet there is limited information on their safety and adverse effect profile. Signals of safety concerns associated with medicines can arise through analysis of reports of suspected adverse drug reactions (ADRs) submitted to national pharmacovigilance centres by health professionals, including pharmacists and the public. However, typically such systems experience substantial under-reporting for NHPs. The objective of this paper is to explore pharmacists' experiences with and responses to receiving or identifying reports of suspected ADRs associated with NHPs from pharmacy customers. METHODS: A qualitative study in which in-depth, semi-structured interviews were conducted with 12 community pharmacists in Toronto, Canada. KEY FINDINGS: Pharmacists generally did not submit reports of adverse events associated with NHPs to the national ADR reporting system and cited several barriers, including lack of time, complexity of the reporting process and lack of knowledge about NHPs. Pharmacists who accepted responsibility for adverse event reporting appeared to have different perceptions of their professional role: they saw themselves as 'knowledge generators', contributing to overall healthcare knowledge. CONCLUSIONS: Reporting behaviour for suspected ADRs associated with NHPs may be explained by a pharmacist's perception of his/her professional role and perceptions of the relative importance of generating knowledge to share in the wider system of health care.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".