Genetics and Personal Insurance: the Perspectives of Canadian Cancer Genetic Counselors
Bibliographic record
Abstract
Genetic discrimination in the context of genetic testing has been identified as a concern for symptomatic and asymptomatic individuals for more than three decades. Genetic counselors are often the health care professionals who discuss risks and benefits of genetic testing with patients, thereby making them most appropriate to address patient concerns about genetics and personal insurance (i.e., life, life as related to mortgage or group insurance, disability, critical illness and travel). A pilot study was conducted to ascertain the current practices of Canadian cancer genetic counselors in regard to their discussions with patients about genetic testing and access to personal insurance. Among the 36 counselors surveyed, 100 % reported discussing the issue of genetic testing and personal insurance with their patients. Several factors influenced the content, depth and length of these discussions including age, cancer status, family members, and patients' current and future insurance needs. Counselors reported discussing with patients the possible impact of genetic test results on access to personal insurance, possible access and use of patient genetic information by insurance companies, and whom patients should contact if they have additional questions. The most commonly reported inquiries from patients included questions about the possible impact of genetic testing on their ability to obtain insurance, and the insurability of family members. While 28 % of counselors reported having been contacted by an insurer requesting access to patient information, only one counselor was aware of or could recall the outcome of such a request. This pilot study revealed that issues concerning genetics and personal insurance are commonly discussed in Canadian cancer genetic counseling sessions. Counselors furthermore expressed a need for additional educational resources on the topic of genetics and personal insurance for themselves and their patients.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".