MétaCan
Menu
Back to cohort
Record W2027522354 · doi:10.3821/1913-701x-145.1.40

Results of a National Survey on Over-The-Counter Medicines, Part 1: Pharmacist Opinion on Current Scheduling Status

2012· article· en· W2027522354 on OpenAlexafffundvenueabout
Jeff Taylor, Eric Landry, Lyne Lalonde, Ross T. Tsuyuki

Bibliographic record

VenueCanadian Pharmacists Journal / Revue des Pharmaciens du Canada · 2012
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health ResearchCollege of Pharmacy and Nutrition, University of SaskatchewanUniversité de MontréalUniversity of AlbertaUniversity of Saskatchewan
KeywordsPharmacistPharmacyOver-the-counterMedicineFamily medicinePharmaceutical careControl (management)NursingMedical prescriptionComputer science

Abstract

fetched live from OpenAlex

UNLABELLED: > BACKGROUND: OTC medicines make up an important part of the community pharmacy world. As with most aspects of practice, however, hurdles exist that prevent an optimal level of care. OBJECTIVE: To gauge pharmacist agreement on the scheduling status of various OTC medicines. METHODS: Pharmacists across Canada were surveyed by mail. RESULTS: Of the 5037 surveys mailed, 2403 were returned, with 2305 being usable for analysis (response rate of 49.4%). Across 25 agents, pharmacists tended to support existing control for pharmacies (such as Nix crème rinse and minoxidil topical solution) and returning control to pharmacies for unscheduled agents (such as ranitidine 75 mg tablets and nicotine patches). CONCLUSIONS: Pharmacists generally favour tighter control of OTC agents, especially those that are unscheduled. This hopefully reflects pharmacist desire to ensure their proper selection and use.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.549
Threshold uncertainty score0.908

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.182
GPT teacher head0.408
Teacher spread0.226 · 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

Citations7
Published2012
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Pharmacists Journal / Revue des Pharmaciens du CanadaSame topicPharmaceutical studies and practicesFrench-language works237,207