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Record W2997312094 · doi:10.15171/ijhpm.2019.146

National Pharmacare in Canada: Equality or Equity, Accessibility or Affordability Comment on "Universal Pharmacare in Canada: A Prescription for Equity in Healthcare"

2020· letter· en· W2997312094 on OpenAlexaffabout
Nigel S. B. Rawson

Bibliographic record

VenueInternational Journal of Health Policy and Management · 2020
Typeletter
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsFraser InstituteCanadian Institute for Health Information
Fundersnot available
KeywordsEquity (law)Medical prescriptionGovernment (linguistics)Health careBusinessPublic administrationPrescription drugPublic relationsEconomic growthPolitical scienceMedicineEconomicsLawNursing

Abstract

fetched live from OpenAlex

Canada's federal government intends to take steps to implement national pharmacare so that all Canadians have prescription drug coverage they need at an affordable price. Relatively limited funds have so far been pledged to support national pharmacare, which raises the question: what kind of program is envisioned? Since the government has already introduced regulations intended to reduce new drug prices drastically, national pharmacare seems likely to be a basic system designed to assist low-income Canadians with accessing primary care medicines. What Canadians actually need is a system that provides access to the medicine considered appropriate by the patient and their healthcare provider for the patient's specific condition. Equitable national pharmacare will not be achieved if patients are denied access to new high-cost specialized medicines that can improve or extend their lives, any more than if patients who cannot afford basic drugs are not helped.

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)0.006
metaresearch head score (Gemma)0.020
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 score0.949
Threshold uncertainty score0.373

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0170.006
Scholarly communication0.0060.003
Open science0.0040.002
Research integrity0.0600.031
Insufficient payload (model declined to judge)0.0100.003

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 head0.582
GPT teacher head0.558
Teacher spread0.024 · 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

Citations11
Published2020
Admission routes2
Has abstractyes

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