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Record W4387821399 · doi:10.51731/cjht.2023.756

Equity in Health Technology Assessment

2023· article· en· W4387821399 on OpenAlexaboutno aff
Farah Husein

Bibliographic record

VenueCanadian Journal of Health Technologies · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsRedressEquity (law)Health equityHealth carePsychological interventionHealth technologyBusinessPublic relationsPublic economicsActuarial sciencePolitical scienceMedicineNursingEconomic growthEconomics

Abstract

fetched live from OpenAlex

Equity-focused health technology assessment (HTA) can help ensure that health care interventions are evaluated and implemented fairly and that health systems are aware of and can take measures to redress inequities, where possible, to achieve the best health outcomes for everyone. CADTH has made a commitment to apply a lens of equity and inclusiveness throughout the organization, to foster health systems that include diverse persons across Canada and that can better respond to their self-identified priorities and health needs.

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)0.078
metaresearch head score (Gemma)0.141
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 score0.078
Threshold uncertainty score0.412

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0780.141
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0020.014
Scholarly communication0.0100.011
Open science0.0020.008
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0120.001

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 head0.503
GPT teacher head0.522
Teacher spread0.019 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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