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Record W2067182354 · doi:10.1186/1472-6882-10-8

Consumers of natural health products: natural-born pharmacovigilantes?

2010· article· en· W2067182354 on OpenAlexafffundabout
Rishma Walji, Heather Boon, Joanne Barnes, Zubin Austin, Sandy Welsh, G. Ross Baker

Bibliographic record

VenueBMC Complementary and Alternative Medicine · 2010
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacovigilance and Adverse Drug Reactions
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsPharmacovigilanceMedicineHealth careFamily medicineAlternative medicineQualitative researchNursingDrugPharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: Natural health products (NHPs), such as herbal medicines and vitamins, are widely available over-the-counter and are often purchased by consumers without advice from a healthcare provider. This study examined how consumers respond when they believe they have experienced NHP-related adverse drug reactions (ADRs) in order to determine how to improve current safety monitoring strategies. METHODS: Qualitative semi-structured interviews were conducted with twelve consumers who had experienced a self-identified NHP-related ADR. Key emergent themes were identified and coded using content analysis techniques. RESULTS: Consumers were generally not comfortable enough with their conventional health care providers to discuss their NHP-related ADRs. Consumers reported being more comfortable discussing NHP-related ADRs with personnel from health food stores, friends or family with whom they had developed trusted relationships. No one reported their suspected ADR to Health Canada and most did not know this was possible. CONCLUSION: Consumers generally did not report their suspected NHP-related ADRs to healthcare providers or to Health Canada. Passive reporting systems for collecting information on NHP-related ADRs cannot be effective if consumers who experience NHP-related ADRs do not report their experiences. Healthcare providers, health food store personnel, manufacturers and other stakeholders also need to take responsibility for reporting ADRs in order to improve current pharmacovigilance of NHPs.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.147
GPT teacher head0.489
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations40
Published2010
Admission routes3
Has abstractyes

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