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Record W2110023126 · doi:10.1215/03616878-25-6-1083

Technology Assessment and the Sociopolitics of Health Technologies

2000· article· en· W2110023126 on OpenAlexaff
Pascale Lehoux, Stuart Blume

Bibliographic record

VenueJournal of Health Politics Policy and Law · 2000
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHealth technologyPoliticsHealth careTechnology assessmentSet (abstract data type)Process (computing)Public relationsPolitical scienceBiomedical technologyEngineering ethicsPsychologyComputer scienceEngineeringLaw

Abstract

fetched live from OpenAlex

In a growing number of countries, health technology assessment (HTA) has come to be seen as a vital component in policy making. Even though the assessment of the social, political, and ethical aspects of health technology is listed as one of its main objectives, in practice, the integration of such dimensions into HTA remains limited. Recent social scientific research on the inherently political nature of technology strongly supports such a comprehensive approach. The growing claims by and on behalf of consumer groups also suggest that HTA should be informed by a broader set of perspectives. Using the example of the cochlear implant in children, this essay compares the professed objectives of HTA with typical practice and explores possible explanations for the discrepancies observed. A second example, home telemonitoring for elderly persons, demonstrates how the types of evidence considered by HTA and the process through which assessments are produced may be reconsidered. We argue for the formal integration of the sociopolitical dimensions of health care technologies into assessments. The ability of HTA to more fully address important issues from a public policy point of view will increase by making explicit the sociopolitical nature of health care technologies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.837
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.246
GPT teacher head0.494
Teacher spread0.249 · 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 designTheoretical or conceptual
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

Citations148
Published2000
Admission routes1
Has abstractyes

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