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Record W4387002235 · doi:10.1111/1468-0009.12674

Building High‐Performing Primary Care Systems: After a Decade of Policy Change, Is Canada “Walking the Talk?”

2023· review· en· W4387002235 on OpenAlexafffundabout
Monica Aggarwal, Brian Hutchison, Reham Abdelhalim, G. Ross Baker

Bibliographic record

VenueMilbank Quarterly · 2023
Typereview
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMcMaster UniversityUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsContext (archaeology)Health carePrimary careHealth care reformPublic relationsBusinessMedical homeMedicineNursingHealth policyPolitical scienceEconomic growthPublic healthFamily medicineGeographyEconomics

Abstract

fetched live from OpenAlex

Policy Points Considerable investments have been made to build high-performing primary care systems in Canada. However, little is known about the extent to which change has occurred over the last decade with implementing programs and policies across all 13 provincial and territorial jurisdictions. There is significant variation in the degree of implementation of structural features of high-performing primary care systems across Canada. This study provides evidence on the state of primary care reform in Canada and offers insights into the opportunities based on changes that governments elsewhere have made to advance primary care transformation. CONTEXT: Despite significant investments to transform primary care, Canada lags behind its peers in providing timely access to regular doctors or places of care, timely access to care, developing interprofessional teams, and communication across health care settings. This study examines changes over the last decade (2012 to 2021) in policies across 13 provincial and territorial jurisdictions that address the structural features of high-performing primary care systems. METHODS: A multiple comparative case study approach was used to explore changes in primary care delivery across 13 Canadian jurisdictions. Each case consisted of (1) qualitative interviews with academics, provincial health care leaders, and health care professionals and (2) a literature review of policies and innovations. Data for each case were thematically analyzed within and across cases, using 12 structural features of high-performing primary care systems to describe each case and assess changes over time. FINDINGS: The most significant changes include adopting electronic medical records, investments in quality improvement training and support, and developing interprofessional teams. Progress was more limited in implementing primary care governance mechanisms, system coordination, patient enrollment, and payment models. The rate of change was slowest for patient engagement, leadership development, performance measurement, research capacity, and systematic evaluation of innovation. CONCLUSIONS: Progress toward building high-performing primary care systems in Canada has been slow and variable, with limited change in the organization and delivery of primary care. Canada's experience can inform innovation internationally by demonstrating how preexisting policy legacies constrain the possibilities for widespread primary care reform, with progress less pronounced in the attributes that impact physician autonomy. To accelerate primary care transformation in Canada and abroad, a national strategy and performance measurement framework is needed based on meaningful engagement of patients and other stakeholders. This must be accompanied by targeted funding investments and building strong data infrastructure for performance measurement to support rigorous research.

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)0.009
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.757
Threshold uncertainty score0.878

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.007
Science and technology studies0.0260.018
Scholarly communication0.0150.005
Open science0.0040.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.079
GPT teacher head0.420
Teacher spread0.341 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations46
Published2023
Admission routes3
Has abstractyes

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