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Record W2970864854 · doi:10.13162/hro-ors.v7i2.3817

Putting National Pharmacare on the Federal Agenda: Creation of an Advisory Council

2019· article· fr· W2970864854 on OpenAlexaffvenueabout
Michel Grignon, Christopher J. Longo, Gregory P. Marchildon, Stephen Officer

Bibliographic record

VenueHealth Reform Observer - Observatoire des Réformes de Santé · 2019
Typearticle
Languagefr
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsUniversity of TorontoMcMaster University
Fundersnot available
KeywordsAdvisory committeePolitical sciencePublic administrationResearch councilPublic relationsGovernment (linguistics)

Abstract

fetched live from OpenAlex

In June 2018, the Federal Government of Canada created an Advisory Council on the Implementation of National Pharmacare, scheduled to report one year later on the best strategy to implement such a program and give Canadians access to affordable prescription drugs outside of the hospital. The current state of coverage through employer-sponsored plans and public plans conditional on income and age is increasingly perceived as unfair and inefficient. The Council used a mixture of expert consultations, an online survey and discussion forums for the general public to explore three options: a universal public plan, a catastrophic spending plan, or a patching of current coverage to include the non-covered. Creating such a Council was seen by the government as a solution for navigating a complex policy problem with difficult federal-provincial dimensions.

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)0.071
metaresearch head score (Gemma)0.075
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: Empirical · Consensus signal: none
Teacher disagreement score0.927
Threshold uncertainty score0.949

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0710.075
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0290.006
Scholarly communication0.0120.007
Open science0.0040.012
Research integrity0.0220.030
Insufficient payload (model declined to judge)0.0100.002

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 head0.272
GPT teacher head0.456
Teacher spread0.184 · 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
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
Published2019
Admission routes3
Has abstractyes

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