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Record W4295358339 · doi:10.12927/hcpol.2022.26908

Commentary: Burning Platforms, Icebergs and Tipping Points – Canada Needs a Single Socially Accountable Healthcare System

2022· letter· en· W4295358339 on OpenAlexaffvenueabout
Roger Strasser

Bibliographic record

VenueHealthcare policy · 2022
Typeletter
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsNOSM UniversityUniversity of Sudbury
Fundersnot available
KeywordsChorusHealth careHealthcare systemPublic relationsBusinessPolitical scienceLaw

Abstract

fetched live from OpenAlex

Leslie et al.' s (2022) article caused me to reflect on the complexities and contradictions that are Canada.Healthcare in Canada is a hodgepodge of different health systems all assembled under the umbrella of the Canada Health Act (1985).Canadians expect medicare to deliver high-quality healthcare close to home wherever they live.For this aspiration to become a reality, there needs to be a single pan-Canadian health system focussed on the health needs of the populations being served.This socially accountable healthcare system is likely to be achieved only if there is a chorus of support across Canada for meaningful pan-Canadian health reforms. RésuméL' article de Leslie et al. (2022) me porte à réfléchir aux complexités et contradictions qui caractérisent le Canada.Les soins de santé y sont un méli-mélo de plusieurs systèmes de santé, tous réunis sous l'égide de la Loi canadienne sur la santé (1985).Les Canadiens s' attendent à ce que l' assurance maladie fournisse des soins de haute qualité près de chez eux, où qu'ils vivent.Pour que ce souhait devienne réalité, il faut un système de santé pancanadien unique axé sur les besoins des populations desservies.Ce système de santé socialement responsable ne sera atteint que si les grandes réformes pancanadiennes de la santé bénéficient du soutien d' une pluralité de personnes au Canada.

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)0.010
metaresearch head score (Gemma)0.043
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 score0.460
Threshold uncertainty score0.926

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0190.010
Scholarly communication0.0070.007
Open science0.0060.003
Research integrity0.0920.072
Insufficient payload (model declined to judge)0.0100.004

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 head0.055
GPT teacher head0.368
Teacher spread0.313 · 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
Published2022
Admission routes3
Has abstractyes

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