Elective genetic testing: Genetics professionals’ perspectives and practices
Bibliographic record
Abstract
Elective genetic testing (EGT) to identify disease risk in individuals who may or may not meet clinical criteria for testing is increasingly being offered in clinical practice. However, little is known about how EGT is currently implemented and how genetics professionals perceive this type of testing. We conducted a mixed-methods survey study to evaluate genetics professionals' perspectives and attitudes about EGT and describe the current landscape of EGT practices in the United States (U.S.) and Canada. Six clinical geneticists and 131 genetic counselors responded to the online survey, among whom 44% reported offering EGT in their practice. Over 84% of survey respondents agreed that EGT may improve health outcomes and understanding of genotype-phenotype correlations, and 85% agreed that potential risks include result misinterpretation and contribution to economic health disparities. Though most respondents felt comfortable providing pretest (77%) and post-test (86%) counseling for EGT, lack of provider resources (such as time and personnel) and prioritization of diagnostic testing were cited most frequently in free-text responses as reasons for not offering EGT. Of those offering EGT, 88% reported positive overall experiences. Qualitative analysis of open-ended questions identified benefits of EGT as expanding access to genetic testing, providing potential health benefits, and providing psychological benefits for patients. Disadvantages included prohibitive costs, limited clinical utility, and strain on resources. Overall, we found that genetics providers perceive both potential benefits and harms of EGT and that those offering this testing had generally positive experiences, although ethical reservations and practical limitations exist.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".