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Record W2908031851 · doi:10.1017/s0266462318001770

OP175 A National Perspective On Criteria And Methods For Resource Allocation

2018· article· en· W2908031851 on OpenAlexaboutno aff
Mathieu Roy, Véronique Déry, Isabelle Ganache, Véronique Gagné, Ghislaine Cleret de Langavant

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsDeliberationViewpointsPopulationPublic relationsFocus groupHealth carePublic healthMedicinePsychologyManagement sciencePolitical scienceBusinessNursingMarketingEconomicsEnvironmental healthLaw

Abstract

fetched live from OpenAlex

Introduction: Decisions about which health and social services to include in the publicly funded services basket are complex. Several criteria need to be taken into account in decision-making (DM), as well as ethical, economic and organizational issues. Nowadays a global consensus supports the view that citizens’ values and preferences must guide DM. To elicit these values and concerns regarding publicly funded services, the Quebec Health and Welfare Commissioner recently conducted a vast public consultation on the population viewpoints. Parts of this consultation targeted criteria for DM, approaches to assess new or current services and perspectives on appropriateness of care. Methods: Various consultation methods were used in complementary steps: a representative population survey (n=1850), six regional focus groups (n=62), a call for briefs (n=52) for groups that wished to share their views, consultation meetings (n=35) with diverse stakeholders and a call for personal accounts (n=2633). It also held five deliberation sessions (18 citizens and 9 experts) over the course of the project on major related issues. Results: The need to ensure the appropriateness of covered services was one of the strongest themes emerging from the consultation. Citizens want that the appropriateness evaluation be carried out under certain conditions: transparently, in explicit DM processes, using criteria that are clear and adaptable according to the disease or problem. The whole evaluation process needs to be well documented, showing clearly the data used and rejected, so that they can understand the decision and see on what basis it is supported. Among the usual criteria for DM, those related to cost are less valued whereas others are considered incomplete. Conclusions: Citizens have clear viewpoints and expectations regarding DM criteria and processes for resource allocation. Decision-makers must take them into account to ensure that the basket of insured services is representative of social values and preferences.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.099
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.128
Threshold uncertainty score0.521

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0990.060
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0110.013
Science and technology studies0.0080.045
Scholarly communication0.0270.020
Open science0.0080.012
Research integrity0.0190.014
Insufficient payload (model declined to judge)0.0230.005

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 head0.225
GPT teacher head0.598
Teacher spread0.373 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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