MétaCan
Menu
Back to cohort
Record W4387577964 · doi:10.12927/hcpol.2023.27179

Have Primary Care Renewal Initiatives in Canada Increased Comprehensive Care for Patients with Complex Care Needs? Yes and No

2023· article· en· W4387577964 on OpenAlexaffvenueabout
Jeannie Haggerty, Catherine M. Scott, Amélie Quesnel‐Vallée, Tara L. Stewart, Émilie Dionne, Noushon Farmanara, Yves Couturier

Bibliographic record

VenueHealthcare policy · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversité de SherbrookeBC StudiesUniversité LavalGeorge & Fay Yee Centre for Healthcare InnovationUniversity of British Columbia, Okanagan CampusUniversity of CalgaryMcGill University Health Centre
Fundersnot available
KeywordsPrimary careGovernment (linguistics)Health careIntegrated careBusinessNorm (philosophy)Social needsPrimary health careSocial careHealth care deliveryNursingPublic relationsMedicineEconomic growthPolitical scienceFamily medicineEconomics

Abstract

fetched live from OpenAlex

The First Ministers Health Accords of 2001 through 2003 (Health Canada 2006) launched the renewal of primary care toward more comprehensive care delivery models. We scanned government websites in the 10 Canadian provinces to assess how comprehensive and integrated renewal models were for health and social services in 2018. More comprehensive primary care delivery models were the norm in five out of 10 provinces. The policy approaches were: (1) expanding traditional family practice; (2) creating primary care networks; and (3) increasing the number of community health centres, which provide the broadest range of health and social care. Integration initiatives were limited to medical services. Additional financial and policy investments will be required to meet the comprehensive needs of patients with complex health and social needs at a system level.

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)0.030
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.900
Threshold uncertainty score0.727

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0060.003
Scholarly communication0.0050.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.056
GPT teacher head0.393
Teacher spread0.337 · 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

Citations4
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueHealthcare policySame topicPrimary Care and Health OutcomesFrench-language works237,207