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Record W2072054217 · doi:10.12927/hcq..18927

Better for Ourselves and Better for Our Patients: Chronic Disease Management in Primary Care Networks

2007· article· en· W2072054217 on OpenAlexaffabout
Barbara Every

Bibliographic record

VenueHealthcare Quarterly · 2007
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsCapital District Health Authority
Fundersnot available
KeywordsMedicinePrimary careCornerstoneGeneral partnershipDisease managementChronic diseaseDiseaseNursingFamily medicineService (business)Chronic careHealth careBusinessPathologyPolitical science

Abstract

fetched live from OpenAlex

Capital Health in Edmonton, Alberta, implemented a system-wide chronic disease management model to support people with chronic disease and their primary care physicians. Groups of family physicians, in partnership with the health region, developed primary care networks to provide services that are customized to meet the priorities of the local community. Management of chronic disease is a cornerstone service, and diabetes management is the most fully developed program. Key to its success are standardized protocols, consistent follow-up and patient education by trained primary care nurses. This model will be used as a template for the management of other chronic diseases.

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)0.007
metaresearch head score (Gemma)0.013
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 score0.075
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.003
Scholarly communication0.0070.005
Open science0.0010.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.012
GPT teacher head0.299
Teacher spread0.286 · 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

Citations13
Published2007
Admission routes2
Has abstractyes

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