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Record W2112172168 · doi:10.1017/s0266462309090424

Health technology assessment in Canada

2009· article· en· W2112172168 on OpenAlexaffabout
Renaldo N. Battista, Brigitte Côté, Matthew Hodge, Don Husereau

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsCanadian Agency for Drugs and Technologies in HealthMcMaster UniversityUniversité de Montréal
Fundersnot available
KeywordsHealth technologyGovernment (linguistics)ModalitiesHealth careBusinessPublic administrationEconomic growthPublic healthTechnology assessmentPublic relationsPublic economicsPolitical scienceMedicineEconomicsNursingSociology

Abstract

fetched live from OpenAlex

Canada's health system is a unique combination of public financing and private provision. With the significant government role in financing health services, health technology assessment (HTA) has found a ready audience as a form of policy research. In addition, Canada has been a leader in HTA and is entering a phase of deepening and maturation of HTA activities. The relative absence of dramatic change in the overall health system, coupled with public faith in the Canadian approach has been favorable to HTA's development in Canada. Emerging issues, beyond the demographic and economic pressures facing all Organisation for Economic Co-operation and Development health systems, include the convergence of assessment modalities and organizations for drug and nondrug technologies, increasing public concerns about the viability of Canada's approach to healthcare services, and the transition of HTA from an activity targeting macro-level policy makers to a management tool for healthcare facilities and providers.

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)0.008
metaresearch head score (Gemma)0.043
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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.811
Threshold uncertainty score0.941

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.016
Science and technology studies0.0060.002
Scholarly communication0.0100.002
Open science0.0020.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0220.003

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 head0.120
GPT teacher head0.478
Teacher spread0.358 · 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
GenreReview

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

Citations41
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Technology Assessment in Health CareSame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207