MétaCan
Menu
Back to cohort
Record W2036788901 · doi:10.1258/jhsrp.2012.012031

‘Innovation’ in health care coverage decisions: All talk and no substance?

2012· article· en· W2036788901 on OpenAlexafffundabout
Stirling Bryan, Helen Lee, Craig Mitton

Bibliographic record

VenueJournal of Health Services Research & Policy · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsVancouver Coastal HealthVancouver Coastal Health Research InstituteUniversity of British Columbia
FundersPfizer CanadaMichael Smith Health Research BC
KeywordsLegitimacyExcellenceOrder (exchange)Health careValue (mathematics)GuidelineBusinessHealth technologyPublic economicsPublic relationsMarketingActuarial scienceEconomicsMedicinePolitical scienceFinanceEconomic growthPolitics

Abstract

fetched live from OpenAlex

There has been much discussion recently about 'innovation', or more precisely the lack of it, in pharmaceuticals and devices in health care. The concern has been expressed by national guideline bodies, such as the Common Drugs Review in Canada and the National Institute for Health & Clinical Excellence in the UK, applying strict cost-effectiveness criteria in their decision-making and, therefore, failing adequately to recognize the full benefits that come from innovation. In order to explore the legitimacy of such claims, we first define innovation, and second, explore the basis for assuming an independent and separable social value associated with innovation. We conclude that demands relating to innovation, such as relaxation of thresholds and premium prices for innovatory products, remain hollow until we have a compelling case on the demand side for a separable social value on 'innovation'. We see no such case currently.

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)0.132
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.132
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0040.039
Scholarly communication0.0120.029
Open science0.0020.007
Research integrity0.0170.016
Insufficient payload (model declined to judge)0.0110.001

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 head0.424
GPT teacher head0.553
Teacher spread0.130 · 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 designQualitative
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

Citations15
Published2012
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Health Services Research & PolicySame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207