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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 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)0.005
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.304
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 teacher head, not a consensus.

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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