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Record W7074086976

Risk Perception of Prescription Drugs: Report on a Survey in Canada

2017· article· en· W7074086976 on OpenAlexaboutno aff

Bibliographic record

VenueScholars' Bank (University of Oregon) · 2017
Typearticle
Languageen
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsnot available
Fundersnot available
KeywordsMedical prescriptionPerceptionSample (material)Risk perceptionPrescription drugPublic healthSuicide preventionOccupational safety and health
DOInot available

Abstract

fetched live from OpenAlex

A representative of the Canadian public was interviewed to determine their attitudes and perceptions of the risks and benefits from prescription drugs. In general, prescription drugs, with the exception of sleeping pills, antidepressants, and tranquilizers, were perceived to be high in benefit and low in risk. They appeared to be sharply differentiated from other chemicals and from illicit drugs. Perceptions varied somewhat according to geographic region, age, gender, education and tendency toward political activism on health issues. Despite the general acceptance of drug risks, respondents were very quick to call for withdrawal from the market of a drug suspected of causing fatal reactions in some patients. Evidence for safety and efficacy, in combination with warning information, appeared to make these concerned individuals much more tolerant of the risks from such a drug. Practical implications of these results and the need for further research on risk/benefit perception are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.011
GPT teacher head0.186
Teacher spread0.174 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations48
Published2017
Admission routes1
Has abstractyes

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