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Record W4386562280 · doi:10.12927/hcpol.2023.27162

Reversing the Stigma around Canada’s Poor-Performing Healthcare Systems

2023· editorial· en· W4386562280 on OpenAlexvenueaboutno aff
Jason M. Sutherland

Bibliographic record

VenueHealthcare policy · 2023
Typeeditorial
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsHealth carePandemicStigma (botany)BusinessCoronavirus disease 2019 (COVID-19)Public economicsEconomic growthEconomicsMedicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

R ecent statistics report that healthcare spending growth is persistently high; in recent years, spending growth exceeded 5% (CIHI 2022).Some portion of the outsized growth can be attributed to the COVID-19 pandemic, though the cause is irrelevant.High spending growth in the healthcare sector is not a good prospect for taxpayers or for education and social programs competing for the same pot of money.Spending is important, but it is not the only attribute for measuring the success of our provinces' and territories' efforts to fund healthcare services that maintain or improve their populations' health.Access to care and the quality of that care are important indicators of performance.Unfortunately, for Canadians, performance on these indicators is also not highly regarded.The oft-cited Commonwealth Fund data recently noted that Canada has the dubious ranking of the tenth lowest-performing health system of the 11 countries examined (Schneider et al. 2021).To borrow a hockey catchphrase, how do governments' health systems get out of the penalty box and make the substantial reforms needed to stop performing so poorly?In my opinion, governments need an aggressive multi-pronged strategy to catch up with the performance of other countries' health systems.Canadians should not continue to accept slow and incremental gains through low-intensity policies or "value veneers" (Batniji and Shrank 2023; Pandey et al. 2023).A well-funded strategy for improving health system performance would apply policies in a number of key areas simultaneously to address underlying factors (Drummond et al. 2023). Social CareTo improve health system performance, reforms must expand outside healthcare.Reforms should include provincial and territorial social care systems that affect their residents' health and well-being.Not addressing residents' social determinants of health, such as poverty or homelessness, will perpetuate clogged emergency departments and overreliance on acute care.

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)0.043
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.974
Threshold uncertainty score0.569

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.043
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.003
Science and technology studies0.0140.010
Scholarly communication0.0130.006
Open science0.0060.003
Research integrity0.0350.045
Insufficient payload (model declined to judge)0.0080.004

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 head0.066
GPT teacher head0.439
Teacher spread0.373 · 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
GenreEditorial

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

Citations0
Published2023
Admission routes2
Has abstractyes

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