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Record W4380730451 · doi:10.1177/08404704231178456

Designing systems for the care we need: A transformation journey in Southwestern Ontario

2023· article· en· W4380730451 on OpenAlexaffabout
Shannon L. Sibbald, Jacobi Elliott, Alexander Smith, Mulugeta Bayisa Chala, Nancy Dool Kontio, Amber Alpaugh-Bishop, Sarah Jarmain, Atharv Joshi, Mike McMahon, Matthew J. Meyer

Bibliographic record

VenueHealthcare Management Forum · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMiddlesex London Health UnitSt Joseph's Health CareUniversity of TorontoLondon Health Sciences CentreThames Valley Children's CentreWestern University
Fundersnot available
KeywordsHealth careHealthcare systemIntegrated careNursingPrimary careHealth care deliveryBusinessMedicineGerontologyFamily medicinePolitical science

Abstract

fetched live from OpenAlex

Primary care is considered the foundation of any health system. In Ontario, Canada Bills 41 and 74 introduced in 2016 and 2019, respectively, aimed to move towards a primary care-focused and sustainable integrated care approach designed around the needs of local populations. These bills collectively set the stage for integrated care and population health management in Ontario, with Ontario Health Teams (OHTs) introduced as a model of integrated care delivery systems. OHTs aim to streamline patient connectivity through the healthcare system and improve outcomes aligned with the Quadruple Aim. When Ontario released a call for health system partners to apply to become an OHT, providers, administrators, and patient/caregiver partners from the Middlesex-London area were quick to respond. We highlight the critical elements and journey of the Middlesex-London Ontario Health Team since its start.

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)0.011
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.217
Threshold uncertainty score0.909

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0530.026
Scholarly communication0.0130.004
Open science0.0020.010
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.102
GPT teacher head0.399
Teacher spread0.297 · 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 designQualitative
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

Citations3
Published2023
Admission routes2
Has abstractyes

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