Direct-to-Consumer Genetic Tests and Canadian Genetic Counselors: A Pilot Exploration of Professional Roles in Response to Novel Biotechnologies
Bibliographic record
Abstract
The role of genetic counselors is evolving in response to health-related direct-to-consumer genetic tests (DTC-GT). While there is consensus in the literature that pre- and post-DTC-GT genetic counseling would benefit consumers, genetic counselors have reservations about DTC-GTs, and there is a paucity of research on providing DTC-GT counseling. This pilot quantitative survey is the first study to examine Canadian genetic counselors' views on DTC-GTs and how this disruptive biotechnology affects their role, and consumer informed consent and privacy. Canadian genetic counselors are cognizant of the harm to informed consent and privacy associated with DTC-GT, but are hesitant to engage directly, wary of misusing clinical time and resources. However, counselors are open to producing educational materials on DTC-GTs and collaborating with other stakeholders and the DTC-GT industry to support consumers. In this study, practical considerations for DTC-GT counseling sessions are discussed, including the unique needs of DTC-GT patients and the challenges posed by DTC-GTs to the genetic counseling duty to inform. This research benefits genetic counselors and physicians by examining how best to utilize genetic counselors' skills in the DTC-GT context, to minimize burdens on the healthcare system and support DTC-GT consumers.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".