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Record W4282975577 · doi:10.13162/hro-ors.v10i1.4696

Implementing Team-Based Innovation in Primary Health Care in British Columbia

2022· article· en· W4282975577 on OpenAlexaffvenueabout
Alexandra Lukey, Karin Maiwald, Paul Wankah, Mylaine Breton, Peter Hirschkorn, Nelly D. Oelke

Bibliographic record

VenueHealth Reform Observer - Observatoire des Réformes de Santé · 2022
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversité de SherbrookeOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsCapitationGovernment (linguistics)Health careNursingBusinessQuality (philosophy)Primary careTeam compositionPrimary health carePopulationMedicinePublic relationsPolitical scienceKnowledge managementFamily medicineEnvironmental healthComputer science

Abstract

fetched live from OpenAlex

Improving health services integration for patients with complex needs is a national priority in Canada. Health systems in all provinces grapple with the rising complexity of patients and the services they need. Team-based primary health care (PHC) models have been implemented in diverse ways to improve patients' experiences, increase the coordination of care, improve population health and reduce costs. While some provinces have more than two decades of experience with PHC teams, others such as British Colombia (BC) have made changes more recently. We conducted an in-depth analysis of 12 provincial policy documents produced since 2011 to study the evolution of interprofessional models in PHC. BC has integrated team-based care through overarching policy support and funding from the provincial government. Structural practice changes to support team-based care, such as Primary Care Networks (PCNs), were designed to address the quadruple aim, a framework designed to improve health system performance through integrated primary care. Policies have addressed the vision and goals of team-based care, but discussion of processes that support teams, such as a strategy for capitation-based funding and team composition, were non-specific. Finally, there is a significant need for a provincial strategy for continuous quality improvement and evaluation of reforms.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.392
Teacher spread0.358 · 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 teacher head, not a consensus.

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

Citations2
Published2022
Admission routes3
Has abstractyes

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Same venueHealth Reform Observer - Observatoire des Réformes de SantéSame topicInterprofessional Education and CollaborationFrench-language works237,207