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Record W3039381556 · doi:10.1016/j.sapharm.2020.06.027

The role of pharmacists in opioid stewardship: Protocol

2020· article· en· W3039381556 on OpenAlexafffund
Nyasha Gondora, Chiranjeev Sanyal, Caitlin Carter, Ashley Nethercott, Beth Sproule, Dana Turcotte, Katelyn Halpape, Lisa Bishop, Lisa Nissen, Michael A. Beazely, Mona Kwong, Sarah G. Versteeg, Feng Chang

Bibliographic record

VenueResearch in Social and Administrative Pharmacy · 2020
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsBritish Columbia Centre on Substance UseUniversity of SaskatchewanUniversity of ManitobaMemorial University of NewfoundlandCentre for Addiction and Mental HealthUniversity of WaterlooCanadian Pharmacists AssociationApotex (Canada)University of Toronto
FundersAssociation des pharmaciens du Canada
KeywordsStewardship (theology)PsycINFOScope of practiceMedicineScope (computer science)PharmacyHealth careNursingMEDLINEPolitical science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.051
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.089
Threshold uncertainty score0.299

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.073
Meta-epidemiology (narrow)0.0030.005
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0040.004
Science and technology studies0.0080.005
Scholarly communication0.0060.004
Open science0.0030.005
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0890.019

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 head0.259
GPT teacher head0.533
Teacher spread0.274 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations5
Published2020
Admission routes2
Has abstractno

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