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Record W2801866300 · doi:10.1007/s41669-018-0083-2

Developing Accessible, Pictorial Versions of Health-Related Quality-of-Life Instruments Suitable for Economic Evaluation: A Report of Preliminary Studies Conducted in Canada and the United Kingdom

2018· article· en· W2801866300 on OpenAlexafffundabout
David G. T. Whitehurst, Nicholas Latimer, Aura Kagan, Rebecca Palmer, Nina Simmons‐Mackie, J. Charles Victor, Jeffrey S. Hoch

Bibliographic record

VenuePharmacoEconomics - Open · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of TorontoInternational Collaboration On Repair DiscoveriesSimon Fraser UniversityVancouver Coastal Health
FundersNational Institute for Health and Care ResearchOntario Stroke NetworkSFU Community Trust Endowment FundHealth Technology Assessment ProgrammeSimon Fraser UniversityTavistock Trust for AphasiaEuroQol Research FoundationResearch for Patient Benefit ProgrammeOntario Ministry of Health and Long-Term Care
KeywordsValuation (finance)PreferenceProcess (computing)Quality of life (healthcare)PsychologyConceptual frameworkQuality (philosophy)Applied psychologyComputer scienceMedicineNursingBusinessSociologySocial science

Abstract

fetched live from OpenAlex

A key component of the current framework for economic evaluation is the measurement and valuation of health outcomes using generic preference-based health-related quality-of-life (HRQoL) instruments. In 2015, a research synthesis reported the absence of conceptual and empirical research regarding the appropriateness of current preference-based instruments for people with aphasia-a disorder affecting the use and understanding of language-and suggested the development and validation of an accessible, pictorial variant could be an appropriate direction for further research. This paper describes the respective rationale and development process for each of three preliminary studies that have been undertaken to develop pictorial variants of two widely used preference-based HRQoL instruments (EQ-5D-3L and EQ-5D-5L). The paper also proposes next steps for this program of research, drawing on the lessons learned from the preliminary work and the demand for a pictorial preference-based instrument in the research community. Guidance for the use of the preliminary, pictorial instruments is also provided.

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)0.038
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0380.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.686
GPT teacher head0.557
Teacher spread0.129 · 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 designObservational
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

Citations26
Published2018
Admission routes3
Has abstractyes

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