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Record W2730981691 · doi:10.12927/hcpap.2017.25084

Shifting Paradigms in Canadian Healthcare to Support the Scale and Spread of the Innovation Ecosystem

2017· article· en· W2730981691 on OpenAlexvenueaboutno aff
Sarah Padfield

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2017
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsTransformational leadershipHealth careScale (ratio)BusinessKnowledge managementOrder (exchange)Healthcare systemScalabilityProcess managementComputer sciencePublic relationsPolitical scienceEconomicsEconomic growthGeography

Abstract

fetched live from OpenAlex

Health systems and healthcare organizations across Canada have identified the need for innovation to transform healthcare by creating the conditions for successful, scalable and impactful system transformation. Meeting these goals requires adopting new leadership paradigms to shape and design policy frameworks, business models, technical structures and funding approaches. In order to scale innovation and achieve system-wide impact, system leaders will need to create a culture that supports innovation and transformational change.

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)0.029
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.723
Threshold uncertainty score0.839

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0300.024
Scholarly communication0.0210.006
Open science0.0040.013
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0080.001

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 head0.144
GPT teacher head0.416
Teacher spread0.273 · 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 designTheoretical or conceptual
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

Citations2
Published2017
Admission routes2
Has abstractyes

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