Divulgation de l’information génétique en assurances
Bibliographic record
Abstract
Today, medical innovations arising from genetic research include the ability to predict, using genetic testing, the future health of certain individuals in particular as to their risk of developing certain diseases such as breast cancer. These advances have generated several therapeutic benefits but also entail new challenges for individuals. Indeed, genetic results generated may raise additional issues related to the use of this information outside of the therapeutic or medical research contexts. Many third parties such as insurers and employers have shown interest in using this information. In the insurance context, such use is likely to lead to a differential treatment of individuals based on their genetic characteristics at the time of purchase of personal insurance, potentially giving rise to the phenomenon of genetic discrimination. Unlike other jurisdictions, the law in Quebec does not provide specific rules on the use of genetic information. This status quo raises several issues in the context of insurance law. What is the scope of the duty to disclose of an insurance applicant and an insured concerning his genetic risks? What is the role of the insurer in the assessment of genetic risks? The study of various issues related to the possible use of genetic information in personal insurance and the duties of the applicant, the insured and the insurer upon subscription or renewal of an insurance policy reveals several uncertainties that may eventually require further clarifications from the legislator or the courts.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".