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Record W2057999511 · doi:10.1017/s0266462306051427

Survey on the involvement of consumers in health technology assessment programs

2006· article· en· W2057999511 on OpenAlexaff
David Hailey, Margareta Nordwall

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHealth technologyBusinessMarketingPublic relationsMedicineMedical educationHealth carePolitical science

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this study was to obtain information from members of the International Network of Agencies for Health Technology Assessment (INAHTA) on their involvement of consumers (patients, carers, and related organizations) in their programs. METHODS: A questionnaire for a survey was developed and sent to member agencies in October 2005. RESULTS: Of the thirty-seven agencies that provided responses, 57 percent involve consumers in some aspects of their HTA programs and 83 percent intend to involve consumers in the future. Summaries of HTA reports that are intended to be easily understood by consumers are prepared by 49 percent of the agencies, and 36 percent involve consumers in dissemination of HTA material. CONCLUSIONS: Most INAHTA members involve consumers in some aspects of their programs, although not always routinely. Involvement seems likely to increase in the future.

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)0.020
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.227
GPT teacher head0.485
Teacher spread0.258 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations49
Published2006
Admission routes1
Has abstractyes

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