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Record W2725067564 · doi:10.12927/hcpap.2017.25086

A Blueprint for Innovation to Achieve Health System Transformation

2017· article· en· W2725067564 on OpenAlexvenueno aff
Anne Snowdon

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsBlueprintTransformational leadershipTransformation (genetics)BusinessFocus (optics)Knowledge managementProcess managementOrganisational changePolitical scienceEngineeringComputer sciencePublic relations

Abstract

fetched live from OpenAlex

Global health systems are challenged by escalating costs and growing demands for care created by the demands of aging populations and rising rates of chronic illness which place unsustainable pressure on health systems to meet population health needs. To overcome these challenges, transformational change is needed to strengthen health system performance and sustainability. Innovation is widely viewed as the strategy to drive transformational change in health systems; yet to date, innovation has lacked a clearly defined focus or agenda to achieve transformation. An actionable innovation agenda is needed to achieve transformational change for health systems. The key conditions for success as an innovation strategy are examined, including clearly defined innovation objectives, key milestones, and actionable steps every system stakeholder must pursue in order to guide the innovation agenda and ultimately accelerate the transformational changes needed for a sustainable healthcare system that delivers value to populations.

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)0.044
metaresearch head score (Gemma)0.042
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 score0.044
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.042
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0060.036
Scholarly communication0.0170.018
Open science0.0030.016
Research integrity0.0160.017
Insufficient payload (model declined to judge)0.0120.004

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 head0.433
GPT teacher head0.468
Teacher spread0.035 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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