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Record W4412631456 · doi:10.1007/s40258-025-00989-2

Measuring and Valuing Health Using EuroQol Instruments: New Developments 2025 and Beyond

2025· review· en· W4412631456 on OpenAlexaff
Nancy Devlin, Feng Xie, Bernhard Slaap, Elly Stolk

Bibliographic record

VenueApplied Health Economics and Health Policy · 2025
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsMcMaster UniversityImpact
FundersEuroQol Research FoundationUniversity of Melbourne
KeywordsHealth economicsQuality of Life ResearchHealth administrationPublic healthHealth services researchHealth informaticsQuality-adjusted life yearMedicineEnvironmental healthCost effectivenessNursingRisk analysis (engineering)

Abstract

fetched live from OpenAlex

The health-related quality of life (HRQoL) instruments developed by EuroQol, an international not-for-profit organisation, have earned a unique position in health economics and outcomes research. The original instrument, EQ-5D-3L, aimed to provide a concise, generic way of measuring and valuing HRQoL in adults that would enable broad comparability of HRQoL across populations and facilitate estimation of quality-adjusted life years (QALYs). These goals remain central to efforts to develop new instruments; to strengthen methods and evidence in measuring and valuing HRQoL; and to expand the use of HRQoL evidence to improve decision making. These initiatives are facilitated by the EuroQol Research Foundation's funding of research and provision of support for instrument users; the commitment of an international community of researchers; and the support of a professional staff team. This paper provides an overview of EuroQol's current suite of instruments: EQ-5D-3L and EQ-5D-5L (for adults) and EQ-5D-Y-3L and EQ-5D-Y-5L (for children) and key elements of its current research agenda. We summarise research underway to expand measurement to very young children (EQ-TIPS), and to expand what is measured (the EuroQol Health and Wellbeing instrument EQ-HWB; and the EQ-5D Bolt-on Toolbox). Research is also generating new valuation methods-such as the development of discrete choice experiment methods that incorporate duration and account for time preference-and strengthening the application of instruments, e.g., to monitor population health and health inequalities (EQ-DAPHNIE). We conclude by highlighting ongoing challenges and their implications for the future of measurement and valuation of HRQoL.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.000
Bibliometrics0.0020.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.641
GPT teacher head0.523
Teacher spread0.118 · 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 teacher head, not a consensus.

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

Citations8
Published2025
Admission routes1
Has abstractyes

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