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E-Health, Local Governance, and Public-Private Partnering in Ontario

2008· book-chapter· en· W2500614878 on OpenAlexaffabout
Jeffrey Roy

Bibliographic record

VenueIGI Global eBooks · 2008
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCorporate governanceIncentiveWork (physics)Public administrationPublic healthBusinessPrivate sectorPublic relationsPublic sectorHealth carePolitical scienceEconomic growthEconomicsMedicineNursingFinanceEngineering

Abstract

fetched live from OpenAlex

The purpose of this chapter is to undertake a critical examination of the emergence of e-health in the Canadian Province of Ontario. More than solely a technological challenge, the emergence and pursuit of e-health denote a complex governance transformation both within the province’s public sector and in terms of public-private partnering. The Ontario challenge here is complicated by the absence of formal regional mechanisms devoted to health care, a deficiency that has precipitated the creation of Local Health Integration Networks (LHINs) to foster e-health strategies on a subprovincial basis, as well as ongoing difficulties in managing public information technologies. With respect to public-private partnering, a greater regionalization of decision-making and spending authorities, within transparent and locally accountable governance forums, could provide incentives for the private sector to work more directly subprovincially, enjoying greater degrees of freedom for collaboration via more manageable contracting arrangements.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.870
Threshold uncertainty score0.940

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0100.007
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.072
GPT teacher head0.319
Teacher spread0.247 · 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
Published2008
Admission routes2
Has abstractyes

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