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Record W2998618314 · doi:10.12927/hcpap.2019.26029

The Quest for Value in Canadian Healthcare: The Applied Value in Healthcare Framework

2019· article· en· W2998618314 on OpenAlexaffvenueabout
Deirdre McCaughey, Gwen McGhan, Sumedh Bele, Nishan Sharma, Natalie C. Ludlow

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsConceptualizationHealth careValue (mathematics)Government (linguistics)Healthcare systemPublic relationsPolitical scienceComputer scienceArtificial intelligencePhilosophyLaw

Abstract

fetched live from OpenAlex

The exponential rise in healthcare costs in developed nations has sharpened the need for greater "value" in healthcare. Porter's (2010) seminal work is one of the most cited definitions and equation for value-based care. The pursuit of greater value in our healthcare system is of paramount importance, yet translating value-based healthcare (VBHC) into a framework that can be effectively utilized in the Canadian system remains a challenge. To address this challenge, we propose that VBHC can be adapted to fit the Canadian healthcare system through (1) visionary leadership for and conceptualization of VBHC at the federal government level and (2) thoughtful application of VBHC at the provincial government level. Our applied value in healthcare framework serves as a platform from which VBHC initiatives, programs and outcome measures can be systematically organized and executed within provincial healthcare systems. This methodical approach could support both provincial ministries and their health systems in pursuit of VBHC and provide the basis for explicit measurement of VBHC success, thereby helping to address the pressing issue of sustainability of the Canadian healthcare system while optimizing patient-centred outcomes of care.

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)0.014
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.669
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.001

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 head0.085
GPT teacher head0.421
Teacher spread0.336 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations3
Published2019
Admission routes3
Has abstractyes

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