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Record W1985211721 · doi:10.12927/hcpap.2012.22985

Now We Need a Framework for Action

2012· letter· en· W1985211721 on OpenAlexvenueaboutno aff
Stephen Peckham

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2012
Typeletter
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsChapelBridging (networking)Health careAction (physics)Healthcare systemPolitical scienceEngineering ethicsSociologyEngineeringComputer scienceHistoryArt historyComputer securityLaw

Abstract

fetched live from OpenAlex

Over the past decade, there has been growing policy interest in the role of primary care within the Canadian health system, with the recognition that it has not been fully integrated and that its full potential has not been developed. The framework of Kates et al. starts to address some key organizational issues, by placing the patient, family and community at the centre of the healthcare system and by combining the Institute for Healthcare Improvement's Triple Aim approach with the Institute of Medicine's six improvement aims. What this framework does not do is provide a model of how to organize or deliver primary care or demonstrate how policy makers and those responsible for designing the healthcare system should structure organizational models, payment systems or accountability structures. Kates et al. have provided a useful first step. We now require a commitment to act.

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)0.075
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.075
Threshold uncertainty score0.398

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.063
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0030.002
Science and technology studies0.0170.073
Scholarly communication0.0250.053
Open science0.0100.022
Research integrity0.0640.093
Insufficient payload (model declined to judge)0.0240.009

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 head0.177
GPT teacher head0.346
Teacher spread0.169 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2012
Admission routes2
Has abstractyes

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