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Record W2730940384 · doi:10.5334/ijic.2514

How do Policy and Institutional Settings Shape Opportunities for Community-Based Primary Health Care? A Comparison of Ontario, Québec and New Zealand

2017· article· en· W2730940384 on OpenAlexaffabout
Tim Tenbensel, Fiona A. Miller, Mylaine Breton, Yves Couturier, Frances Morton-Chang, Toni Ashton, Nicolette Sheridan, A. Paul Williams, Timothy Kenealy, Walter P. Wodchis

Bibliographic record

VenueInternational Journal of Integrated Care · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of TorontoUniversité de Sherbrooke
Fundersnot available
KeywordsFence (mathematics)Community healthHealth careService (business)Primary carePopulationPublic relationsHealth policyRelevance (law)BusinessPublic administrationPopulation healthPolitical scienceEconomic growthMedicineMarketingEconomicsEnvironmental healthFamily medicine

Abstract

fetched live from OpenAlex

Community-based primary health care describes a model of service provision that is oriented to the population health needs and wants of service users and communities, and has particular relevance to supporting the growing proportion of the population with multiple chronic conditions. Internationally, aspirations for community-based primary health care have stimulated local initiatives and influenced the design of policy solutions. However, the ways in which these ideas and influences find their way into policy and practice is strongly mediated by policy settings and institutional legacies of particular jurisdictions. This paper seeks to compare the key institutional and policy features of Ontario, Québec and New Zealand that shape the 'space available' for models of community-based primary health care to take root and develop. Our analysis suggests that two key conditions are the integration of relevant health and social sector organisations, and the range of policy levers that are available and used by governments. New Zealand has the most favourable conditions, and Ontario the least favourable. All jurisdictions, however, share a crucial barrier, namely the 'barbed-wire fence' that separates funding of medical and 'non-medical' primary care services, and the clear interests primary care doctors have in maintaining this fence. Moves in the direction of system-wide community-based primary health care require a gradual dismantling of this fence.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.125
GPT teacher head0.434
Teacher spread0.310 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations30
Published2017
Admission routes2
Has abstractyes

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