MétaCan
Menu
Back to cohort
Record W2106746104 · doi:10.12927/hcpol.2008.20005

The Helix in the Labyrinth: Do We Need Genetic Health Services and Policy Research?

2008· article· en· W2106746104 on OpenAlexaffvenueabout
Fiona A. Miller, Brenda J. Wilson, Jeremy Grimshaw, Renaldo N. Battista, Ingeborg Blancquaert, June Carroll, François Rousseau, Barbara Slater

Bibliographic record

VenueHealthcare policy · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGenomicsHealth policyGenetic engineeringHealth careEngineering ethicsPolitical scienceData scienceKnowledge managementBiologyGeneticsGenomeComputer scienceEngineeringGene

Abstract

fetched live from OpenAlex

In Canada and elsewhere, targeted health services and policy research (HSPR) has been suggested as a means to clarify the health system implications of developments in genetics and genomics. But is such research really needed? We argue that substantial investments in basic genetic and genomic research, coupled with persistent uncertainty about the health system implications of advances in these fields, justify the development of specialized HSPR in genetics and the sustained involvement of the wider HSPR community. Genetic health services and policy research will play a crucial role in informing decision-makers at all levels of the health system about whether and how to integrate developments in genetics, genomics and other complex new 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 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)0.044
metaresearch head score (Gemma)0.095
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: Empirical · Consensus signal: none
Teacher disagreement score0.060
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.095
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.005
Science and technology studies0.0080.031
Scholarly communication0.0180.028
Open science0.0020.007
Research integrity0.0200.016
Insufficient payload (model declined to judge)0.0110.002

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 head0.512
GPT teacher head0.526
Teacher spread0.014 · 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
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

Citations3
Published2008
Admission routes3
Has abstractyes

Explore more

Same venueHealthcare policySame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207