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Record W2750007299 · doi:10.1111/ijpp.12390

Integrating medication samples into the traditional community pharmacy supply chain: a feasibility study

2017· article· en· W2750007299 on OpenAlexaff
Lisa McCarthy, Thomas E. Brown, Natalie Crown, Neil Jobanputra, Michelle Lui, Leigha Laporte, Valerie H. Taylor

Bibliographic record

VenueInternational Journal of Pharmacy Practice · 2017
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsHamilton Health SciencesWomen's College HospitalUniversity of Toronto
FundersAmerican College of Clinical Pharmacy
KeywordsMedicineVoucherPharmacyPharmacistCommunity pharmacistCommunity pharmacyFeelingFamily medicineSampling (signal processing)Pharmaceutical careNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the feasibility of a medication sampling program involving community pharmacists. METHODS: A community pharmacy dispensed samples after receiving a voucher given to patients by prescribers. Surveys explored prescribers' and patients' views about sampling and patients' experiences with the program. KEY FINDINGS: Half of prescribers reported providing samples, yet 15 patients redeemed 18 vouchers over 1 year. Patients expressed favourable views towards sampling and pharmacist involvement, despite more than half (n = 8/15, 53%) feeling that visiting the pharmacy was less convenient. CONCLUSION: A voucher-based medication sampling program based in a community pharmacy is a model integrating pharmacist 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.

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)0.017
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.304
GPT teacher head0.503
Teacher spread0.199 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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