Reporting natural health product related adverse drug reactions: is it the pharmacist's responsibility?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 it