MétaCan
Menu
Back to cohort
Record W2945922267 · doi:10.1177/0840470418824614

Using health technology assessment for better healthcare decisions

2019· article· en· W2945922267 on OpenAlexaffabout
Sheila Tucker, Kelli O’Brien, Heather M. Brown

Bibliographic record

VenueHealthcare Management Forum · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsGovernment of Newfoundland and LabradorCanadian Agency for Drugs and Technologies in Health
Fundersnot available
KeywordsHealth technologyBusinessHealth carePsychological interventionNursingPublic relationsMedicinePolitical scienceEconomic growthEconomics

Abstract

fetched live from OpenAlex

Canadian health leaders can benefit from greater involvement in the design and production of Health Technology Assessment (HTA) through enhanced working relationships with HTA producers. The HTA producers and health leaders have a shared interest in the appropriate use of pharmaceuticals, healthcare devices, and procedures which benefit patients and support sustainable health systems. This article highlights the shared responsibility of HTA producers and decision makers in the appropriate use of these healthcare interventions through an examination of a HTA-informed policy and practice change in the management diabetes in elderly residents in long-term care settings. Consideration is given to the role of the co-responsibility model and LEADS in a Caring Environment framework (LEADS) in helping to facilitate partnerships between decision-makers and HTA producers in the realization of HTA-informed policy and practice.

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)0.055
metaresearch head score (Gemma)0.118
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: Review · Consensus signal: none
Teacher disagreement score0.105
Threshold uncertainty score0.293

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.118
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.008
Science and technology studies0.0020.005
Scholarly communication0.0140.010
Open science0.0020.005
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0140.001

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 head0.418
GPT teacher head0.504
Teacher spread0.086 · 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
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

Citations2
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueHealthcare Management ForumSame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207