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Record W2994183469

Life Insurers' Access to Genetic Information: A Way out of the Stalemate?

2006· article· en· W2994183469 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDigitalGeorgetown (Georgetown University Library) · 2006
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsEugenicsPopulationPolitical scienceDilemmaGenetic discriminationPoliticsEnvironmental ethicsPublic relationsSociologyLawGenetic testingGeneticsBiology
DOInot available

Abstract

fetched live from OpenAlex

Introduction The advent of genomics at the end of the 20th century intensified the existing debate surrounding the social, ethical, and legal issues raised by modern medicine. Genetics increased popular awareness of fundamentally important issues including the redefinition of the human being, eugenics and the organization of healthcare in liberal democracies. The general population, informed through the lens of the media, had to quickly learn and understand the meaning of complex terms and concepts such as cloning, stem cells, gene therapy, or medically assisted reproduction. The societal consequences of scientific developments in the field of genomics were especially visible in the areas of privacy and personal confidentiality. In the mid-1980s, progress in the development of predictive tests for Huntington's disease coincided with the first discussions in specialized fields about the risks of (1) The emerging debate, polarized by the media, became more extensive during the 1990s. Insurers' uncompromising attitude, (2) and the growing concern of the population about the vast possibilities presented by genetics, drove political decision-makers in many countries to legislate in order to address popular concerns. (3) However, despite intensive regulatory activity in Europe and in the United States, the socio-ethical debate on insurance and genetics has barely progressed during the last twenty years. After focussing on certain aspects of the genetics and insurance dilemma, this article will provide a critical analysis of the evolution of the debate in France and Canada. (4) Finally, the author will argue for the need to redefine the dialogue by broadening its scope, adopting a long term perspective and recognizing the need for transparency in order to exit the dead-end we are currently facing. In the current context, genetics could well be hiding a more fundamental debate about the place of insurance in the contemporary democratic state. Characteristics of the Debate The results of two recent North American surveys highlight the general discomfort caused by the fact that insurers could potentially use genetic information for underwriting purposes. (5) However, the two surveys share the same difficulty with regard to defining and circumscribing the precise extent of the popular sentiment. The first shows that although the majority of individuals surveyed oppose insurance companies' access to genetic data, these same individuals still perceive genetic information as being similar to other types of medical information and cannot articulate why it should be treated differently. (6) Although the results of the second study show a certain concern in the population vis-a-vis genetic discrimination, this concern seems to have had only a marginal influence on the surveyed population's decision to undergo genetic testing or not. (7) The discomfort reflected in these and other studies (8) could be explained by the difficulty of defining the concept of The concept of discrimination evokes a different reality for insurers, policy-makers, and the general population. Therefore, a single question about genetic discrimination may be perceived differently by various audiences. (9) For example, in order to assess if a given law or governmental practice is discriminatory or not, the Canadian judiciary will follow article 15(1) of the Canadian Charter of Rights and Freedom (10) and the corresponding case law. In this situation, discrimination is used to indicate a type of distinction that is both unjustified and based on prohibited (or analogous) grounds, i.e., unlawful discrimination. On the other hand, for an insurer, the selection and classification of life insurance applicants based on health factors and risk level is perceived as a kind of discrimination that is both rational and legal, i.e. actuarial discrimination. …

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.001
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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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