Disclosure of insurability risks in research and clinical consent forms
Bibliographic record
Abstract
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.
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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 | 0.198 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".