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Record W2077262353 · doi:10.1586/14737167.2014.894464

Methodological guidance documents for evaluation of ethical considerations in health technology assessment: a systematic review

2014· review· en· W2077262353 on OpenAlexaff
Nazila Assasi, Lisa Schwartz, Jean‐Éric Tarride, Kaitryn Campbell, Ron Goeree

Bibliographic record

VenueExpert Review of Pharmacoeconomics & Outcomes Research · 2014
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsMcMaster UniversityPrograms for Assessment of Technology in Health Research Institute
Fundersnot available
KeywordsEngineering ethicsScope (computer science)Health technologyNormativeContext (archaeology)Management scienceIdentification (biology)Ethical issuesComputer scienceKnowledge managementPolitical scienceHealth careEngineering

Abstract

fetched live from OpenAlex

Despite the advances made in the development of ethical frameworks for health technology assessment (HTA), there is no clear agreement on the scope and details of a practical approach to address ethical aspects in HTA. This systematic review aimed to identify existing guidance documents for incorporation of ethics in HTA to provide an overview of their methodological features. The review identified 43 conceptual frameworks or practical guidelines, varying in their philosophical approach, structure, and comprehensiveness. They were designed for different purposes throughout the HTA process, ranging from helping HTA-producers in identification, appraisal and analysis of ethical data to supporting decision-makers in making value-sensitive decisions. They frequently promoted using analytical methods that combined normative reflection with participatory approaches. The choice of a method for collection and analysis of ethical data seems to depend on the context in which technology is being assessed, the purpose of analysis, and availability of required resources.

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)0.372
metaresearch head score (Gemma)0.108
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.354
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.3720.108
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0200.002
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.753
GPT teacher head0.756
Teacher spread0.003 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
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

Citations65
Published2014
Admission routes1
Has abstractyes

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