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Record W2997416401 · doi:10.1017/s0266462319003349

VP89 A Preliminary Equity Checklist To Support The HTA Process

2019· article· en· W2997416401 on OpenAlexaboutno aff
Maria Benkhalti, Pierre Dagenais

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistEquity (law)Psychological interventionProcess (computing)Health technologyProcess managementPolitical scienceManagement scienceRisk analysis (engineering)BusinessMedicinePsychologyComputer scienceHealth careEngineeringNursing

Abstract

fetched live from OpenAlex

Introduction There is increased recognition of the need to include equity considerations in HTA. Despite this, a recent World Health Organization report has found that this is seldom the case. We developed a preliminary version of an equity checklist in the hopes that tangible guidance will increase such analyses in the future and contribute to smart capability building. Methods The checklist is based on the Equity Framework for HTA developed by Culyer & Bombard (2012). The elements presented in the framework were revised to follow the stepwise HTA process. A comprehensive literature search was used to update and complete the elements. The checklist was then piloted in an HTA in 2018 and subsequently further refined through a workshop during a national HTA conference in Canada. Results These steps resulted in a 27-item checklist leading to consider different aspects of the three major phases in the HTA process. The scoping phase brings questions relative to defining and contextualizing equity, such as highlighting potential minority groups and including vulnerability factors in the logic model. The development phase leads methodological approaches facilitating the analysis of inequities as well as considering contextual realities leading to inequities. The last phase, drafting of recommendations, aims to be aware of the evidence synthesis approaches as well as the various aspects to ensure recommendations consider existing inequities and avoid contributing to their development. Conclusions Given the essence of HTA to protect health by ensuring optimal technologies and interventions are adopted to the benefit of all system users, the consideration of inequities should constitute an integral part of its process. The use of a pragmatic and simple checklist to aid the planning of an HTA could contribute to greater consideration of inequities in the future. A movement in this direction could also lead to greater methodological developments for health equity analysis in HTA.

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)0.118
metaresearch head score (Gemma)0.318
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: Methods · Consensus signal: none
Teacher disagreement score0.118
Threshold uncertainty score0.623

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1180.318
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0140.007
Science and technology studies0.0050.003
Scholarly communication0.0070.008
Open science0.0060.009
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0310.007

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 head0.167
GPT teacher head0.524
Teacher spread0.357 · 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
GenreMethods

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".

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Citations3
Published2019
Admission routes1
Has abstractyes

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