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Record W2010311287 · doi:10.1016/s0840-4704(10)60581-9

Connecting for Change: <i>Networks as a Vehicle for Regional Health Reform</i>

2002· article· en· W2010311287 on OpenAlexaboutno aff
Shehnaz Alidina, Sheila Jarvis, Beverley Nickoloff, Jonathan Tolkin, Joann Trypuc

Bibliographic record

VenueHealthcare Management Forum · 2002
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipQuality (philosophy)BusinessRehabilitationHealth careNursingPublic relationsPolitical scienceEconomic growthMedicineEconomicsFinance

Abstract

fetched live from OpenAlex

The Child Health Network (CHN) for the Greater Toronto Area (GTA) is a partnership of hospital, rehabilitation and community providers committed to developing a regional system to deliver high quality, accessible, family-centred care for mothers, newborns, children and youth. This article reviews the history and model of the CHN, assesses its achievements, and provides insights into the challenges and lessons learned by the network. Stemming from the CHN's commitment to quality, accessibility and efficiency, regionalization of maternal, newborn and children's services is emerging as a success story.

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)0.008
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: none
Teacher disagreement score0.015
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.015
Scholarly communication0.0140.013
Open science0.0010.008
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.146
GPT teacher head0.418
Teacher spread0.272 · 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
Published2002
Admission routes1
Has abstractyes

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