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Record W2322057561 · doi:10.1177/1715163513482708

Federal government unveils proposals to provide “red tape relief” to pharmacies

2013· article· en· W2322057561 on OpenAlexvenueaboutno aff
Kathie Lynas

Bibliographic record

VenueCanadian Pharmacists Journal / Revue des Pharmaciens du Canada · 2013
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPharmacyGovernment (linguistics)Scope (computer science)Medical prescriptionBusinessChemistHealth careMedicinePublic relationsFamily medicinePolitical scienceNursingLaw

Abstract

fetched live from OpenAlex

Regulatory changes designed to provide “red tape relief” will save Canadian pharmacies an estimated $8.7 million a year in administrative costs, according to the federal government. The government announced proposed changes to the Food and Drug Regulations on January 21, 2013, as part of its larger initiative for small business, the Red Tape Reduction Action Plan, announced in October 2012. The new rules would allow provincially regulated pharmacy technicians to transfer prescriptions to another pharmacy and to complete the related paperwork—tasks currently restricted to pharmacists. The changes are to take effect this year in Ontario, Alberta and British Columbia—where pharmacy technicians are regulated—and will apply in other provinces when they implement regulation of their technicians. “This amendment… will enable pharmacists to focus more of their time on direct patient care,” said Dennis Darby, CEO of the Ontario Pharmacists’ Association, in a federal news release. “As key members of the pharmacy team, pharmacy technicians need to be able to practise to their full scope so that pharmacists can provide the best, most efficient health care to patients.”

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)0.009
metaresearch head score (Gemma)0.028
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.963
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0110.003
Scholarly communication0.0070.001
Open science0.0020.002
Research integrity0.0170.009
Insufficient payload (model declined to judge)0.0130.003

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 head0.066
GPT teacher head0.325
Teacher spread0.259 · 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
GenreCommentary

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
Published2013
Admission routes2
Has abstractyes

Explore more

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