Genetics and Insurance Discrimination: Comparative Legislative, Regulatory and Policy Developments and Canadian Options
Bibliographic record
Abstract
Whether insurance companies should have access to genetic test results of insurance applicants and/or should be allowed to impose such testing as part of insurance underwriting remains hotly debated. In Canada, as in other countries with universal health care coverage, the debate focuses on the use of genetics in the context of life insurance and additional health insurance. This article first discusses how human rights law and insurance law provide some protection in Canada against genetic discrimination, even in the absence of specific statutes or regulations. It then highlights why the use of genetic information for private insurance contracts still raises concerns in the context of country with a universal health care system and with some legislative protection. In the second part of the article, various legal and policy options are discussed in comparative perspective. The author analyzes how different options have been implemented in other countries, in particular in Europe. The article describes the experience of these countries with: moratoria on the use of genetic information; industry self-regulation; changes to insurance law, including prohibiting the use of genetic information and setting a ceiling on insurance coverage; and changes to human rights law. The author calls in conclusion for the introduction of a more general regulatory review process for genetic testing.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".