MétaCan
Menu
Back to cohort
Record W2402543137 · doi:10.1080/11287462.2016.1183442

Disclosure of insurability risks in research and clinical consent forms

2016· article· en· W2402543137 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGlobal Bioethics · 2016
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsInsurabilityUnderwritingInformed consentGenetic testingActuarial scienceMedicinePsychologyBusinessInsurance policyAlternative medicinePathologyInsurance law

Abstract

fetched live from OpenAlex

Genetic testing results and research findings raise concerns about access to genetic information by insurers. Recently, the Canadian Life and Health Insurance Association reaffirmed its prerogative to request, for underwriting purposes, the disclosure of clinical and research genetic test results if the participant/patient or his physician has knowledge of the results. Studies have shown that access to genetic information to determine insurability can, in limited instances, lead to actual, or fear of, genetic discrimination, result in individuals refusing to undergo testing or declining participation in genomic research, and being asked to pay higher premiums or denied access to certain types of insurance. Obtaining informed consent for genetic testing and genomic research is crucial and should take into account the potential need to disclose possible insurability risks to patients and participants. Our study analyzed clinical and research consent forms, templates and guidelines from Quebec to investigate two questions: (1) whether consent forms include clauses providing information on potential insurability risks and (2) when such potential risks are included, what information is provided and how it is formulated. Our findings show that current information on insurability risks in Quebec’s forms/guidelines lack coherence, potentially resulting in patients/participants receiving inconsistent information.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.008
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.576
GPT teacher head0.587
Teacher spread0.010 · 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