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National Pharmacare in Canada: 2019 or Bust?

2017· article· en· W6903236311 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Calgary · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPaymentMedical prescriptionPlan (archaeology)Table (database)Government (linguistics)Public policy

Abstract

fetched live from OpenAlex

It is the Canadian public policy issue that rears its head with regularity, never achieving much more than discussion, and yet never going away entirely. The issue is pharmacare, and once again it is back for discussion among academics and policy-makers, and once again it looks like the discussions will not go anywhere anytime soon. The proposal for a publicly funded pharmaceuticalcoverage plan is frequently on the table in Canada, but it still is not in the cards. Canada is the only member country of the Organisation for Economic Cooperation and Development (OECD) with a public health-care system that does not include coverage for pharmaceuticals. As a result, Canada spends markedly less public money than the OECD average on pharmaceuticals (42 per cent of drug payments are public funds, versus the average 70 per cent), although it also spends more than the OECD average on hospitals and doctor visits. Advocates for an expansion of the publicly funded medicare system to include prescription medication note that it has become common for some lower-income Canadians who lack private drug insurance to leave prescriptions unfilled due to the cost, or will miss doses. This affordability problem for lower-income Canadians appears to be getting more serious. However, while Canadians seem to express support for the idea of pharmacare when asked about it in surveys, it remains well behind a list of other improvements to the health-care system that they consider to be of higher priority. They are more interested in improving access and wait times, and they are more concerned about the sustainability of the current system given the increased demands of the aging population. Both employers and workers, meanwhile, also support the existing model of employer-provided drug plans. Perhaps the biggest obstacle for champions of pharmacare, however, is that the term can mean so many different things to different people. There is virtually no consensus on what would even be the appropriate Canadian system, particularly in light of how significant a factor private coverage already is in Canada. A pharmacare plan might include anything from the drastic step of eliminating all private coverage and subsidizing all prescription medicine for all patients regardless of income, to a much narrower program that covers some portion of the cost of only some drugs, for some income levels. There are also countless different possible models between those two. The matter of how much each level of government, provincial/territorial or federal, would be responsible for funding drugs is a whole other, rather thorny matter. The timing of this latest discussion about pharmacare — stimulated mainly by recent proposals and fuelled by the success of the pan-Canadian Pharmaceutical Alliance in negotiating better bulk drug prices — is also particularly unfavourable. There has been a sudden shift in the dynamic between the federal government and the provinces, where before premiers stood together and collectively bargained with the federal government for health-care funding, but recently splintered and are now making individual deals (while Quebec continues to insist that it must have complete freedom from Ottawa to design its own health system). The difficult fiscal situation across Canada, with so many governments running up debts, would also seem to make it highly unlikely that there will be much enthusiasm for embarking on a new and sizeable social program costing billions of dollars a year. Lacking enough leaders to passionately champion it, and with a public generally uninterested and very unclear on what a national pharmacare program would even entail, it seems that the current discussion about implementing a pharmacare system may come to a stall, like so many discussions before it.

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)0.013
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: Other · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.857

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0100.005
Scholarly communication0.0110.006
Open science0.0040.004
Research integrity0.0130.010
Insufficient payload (model declined to judge)0.0720.012

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 head0.023
GPT teacher head0.260
Teacher spread0.237 · 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
GenreOther

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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