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Record W3109363024 · doi:10.13162/hro-ors.v8i2.4219

Integrating Health Services in Ontario Through Mergers and Centralization

2020· article· en· W3109363024 on OpenAlexaffvenueabout
Krithika Ragupathi

Bibliographic record

VenueHealth Reform Observer - Observatoire des Réformes de Santé · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsModernization theoryLegislaturePublic administrationAgency (philosophy)Health careHealth care reformChristian ministryPublic healthBusinessHealth policyPolitical sciencePublic relationsMedicineNursingSociologyLaw

Abstract

fetched live from OpenAlex

On 18 April 2019, the Legislative Assembly of Ontario passed Bill 74, The People’s Health Care Act, which provided new authorities to the Ministry of Health and a newly createdOntario Health super agency to facilitate the integration of health care services across Ontario. This reform represents a shift away from the previous regionalized system of Local Health Integration Networks (LHINs). While centralization is intended to improve efficiency within the system and create a patient-centred model of care, it also equips the Ministry and Ontario Health with greater authority over health agencies. Ontario’s reform represents another move towards centralization in a wave of regionalization reversal that has swept across the country. Implementation of this reform will take several years to roll out. An analysis of centralization reforms in other jurisdictions can provide insight into Ontario’s decision to reform. Though this bill was presented as a modernization of Ontario’s health system to meet people’s needs, a common theme in stakeholders’ opinions is the lack of consultation with the public.

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)0.008
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score0.983

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0070.006
Scholarly communication0.0070.003
Open science0.0010.007
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.088
GPT teacher head0.293
Teacher spread0.205 · 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
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

Citations0
Published2020
Admission routes3
Has abstractyes

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