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Record W2697207264 · doi:10.1177/0020731416681229

Revisiting Health Regionalization in Canada

2016· article· en· W2697207264 on OpenAlexaffabout
Paul Barker, John Church

Bibliographic record

VenueInternational Journal of Health Services · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAutonomyHealth careBusinessPublic healthHealth policyEconomic growthHealth servicesPublic administrationPolitical scienceMedicineEnvironmental healthNursingPopulationEconomics

Abstract

fetched live from OpenAlex

Twenty years ago, many of Canada's provinces began to introduce regional health authorities to address problems with their health care systems. With this action, the provinces sought to achieve advances in community decision-making, the integration of health services, and the provision of care in the home and community. The authorities were also to help restrict health care costs. An assessment of the authorities indicates, however, that over the past two decades they have been unable to meet their objectives. Community representatives continue to play little role in determining the appropriate health services for their regions. Gains have been made towards integrating health services, but the plan for a near seamless set of health services has not been realized. Funding for health services remains focused on hospital and physician care, and health care expenditures have until very recently been little affected by regional authorities. This disappointing performance has caused some provinces to abandon their regional authorities, but this article argues that the provision of greater autonomy and a better public appreciation of their role and potential may lead to more successful regional authorities. Accordingly, the objective of this article is to reveal the shortcomings of regional health authorities in Canada while at the same time arguing that changes can be made to increase the chances of more workable authorities.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.683
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.424
Teacher spread0.387 · 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 teacher head, 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

Citations21
Published2016
Admission routes2
Has abstractyes

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