Genetic Counseling and the Ethical Issues Around Direct to Consumer Genetic Testing
Bibliographic record
Abstract
Over the last several years, direct to consumer(DTC) genetic testing has received increasing attention in the public, healthcare and academic realms. DTC genetic testing companies face considerable criticism and scepticism,particularly from the medical and genetic counseling community. This raises the question of what specific aspects of DTC genetic testing provoke concerns, and conversely,promises, for genetic counselors. This paper addresses this question by exploring DTC genetic testing through an ethic allens. By considering the fundamental ethical approaches influencing genetic counseling (the ethic of care and principle-based ethics) we highlight the specific ethical concerns raised by DTC genetic testing companies. Ultimately,when considering the ethics of DTC testing in a genetic counseling context, we should think of it as a balancing act. We need careful and detailed consideration of the risks and troubling aspects of such testing, as well as the potentially beneficial direct and indirect impacts of the increased availability of DTC genetic testing. As a result it is essential that genetic counselors stay informed and involved in the ongoing debate about DTC genetic testing and DTC companies. Doing so will ensure that the ethical theories and principles fundamental to the profession of genetic counseling are promoted not just in traditional counseling sessions,but also on a broader level. Ultimately this will help ensure that the public enjoys the benefits of an increasingly genetic based healthcare system.
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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 | 0.043 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.050 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.023 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".