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Record W2770833778 · doi:10.55016/ojs/sppp.v2i1.42326

Competition in Canadian Health Care Service Provision: Good, Bad or Indifferent?

2009· article· en· W2770833778 on OpenAlexaffabout
Jane E. Ruseski

Bibliographic record

VenueThe School of Public Policy Publications · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCompetition (biology)BusinessService (business)Health carePublic relationsMarketingEconomicsPolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

Most provincial health care systems in Canada combine public, private non-profit, and private for-profit delivery. In Alberta, the Health Care Protection Act , known as Bill 11, allows the public to purchase certain insured surgical services from private providers. This legislation sparked a heated and ongoing debate in Canada about the role of competition in health care service delivery. The key question asked is what can be gained from introducing competition among hospital and physician services while maintaining a public payment system. This paper evaluates what has been learned from the recent literature on competition in health care markets in the context of expanding the role of the private sector in Alberta. The evidence does not provide a definitive answer. Competition introduced by an expanded private sector is likely to be beneficial on some measures, indifferent on others, but not likely bad.

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)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.956

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0090.013
Scholarly communication0.0090.004
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.067
GPT teacher head0.319
Teacher spread0.251 · 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 designObservational
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
Published2009
Admission routes2
Has abstractyes

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