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Record W4381164196 · doi:10.12927/hcpol.2023.27089

A North Star Vision: Results from a Deliberative Dialogue to Identify Policy Strategies to Improve Value in Healthcare

2023· article· en· W4381164196 on OpenAlexafffundvenueabout
Amity E. Quinn, Rachelle Drummond, Fiona Clement, M. Columbus, Stephana J. Moss, Emily A. FitzGerald, Rukhsaar Daya, Liza Mastikhina, Jeanna Parsons Leigh, Henry T. Stelfox

Bibliographic record

VenueHealthcare policy · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsDalhousie UniversityUniversity of Calgary
FundersCanadian Institutes of Health ResearchUniversity of Calgary
KeywordsEquity (law)Health careValue (mathematics)Public relationsGovernment (linguistics)Healthcare systemHealthcare policyHealth policyPolitical sciencePublic administrationSociologyHealth care reformComputer science

Abstract

fetched live from OpenAlex

= 3) to identify evidence-informed policy options to improve the value of Canadian healthcare. The analysis resulted in three themes: (1) the need for a vision to guide reforms, (2) community-based care and (3) community-engaged care. Results suggest the need for a new paradigm: community-focused health systems. Such a paradigm could serve as a North Star guiding healthcare transformation, improving value by aligning citizen and healthcare system goals, prioritizing spending on services that address the social determinants of health and improving quality and equity.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.795
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.004

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 head0.082
GPT teacher head0.499
Teacher spread0.417 · 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

Citations5
Published2023
Admission routes4
Has abstractyes

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