Utilization of Genetic Counseling after Direct‐to‐Consumer Genetic Testing: Findings from the Impact of Personal Genomics (PGen) Study
Bibliographic record
Abstract
Direct-to-consumer personal genomic testing (DTC-PGT) results lead some individuals to seek genetic counseling (GC), but little is known about these consumers and why they seek GC services. We analyzed survey data pre- and post-PGT from 1026 23andMe and Pathway Genomics customers. Participants were mostly white (91%), female (60%), and of high socioeconomic status (80% college educated, 43% household income of ≥$100,000). After receiving PGT results, 43 participants (4%) made or planned to schedule an appointment with a genetic counselor; 390 (38%) would have used in-person GC had it been available. Compared to non-seekers, GC seekers were younger (mean age of 38 vs 46 years), more frequently had children <18 (26% vs 16%), and were more likely to report previous GC (37% vs 7%) and genetic testing (30% vs 15%). In logistic regression analysis, seeking GC was associated with previous GC use (OR = 6.5, CI = 3.1-13.8), feeling motivated to pursue DTC-PGT for health reasons (OR = 4.3, CI = 1.8-10.1), fair or poor self-reported health (OR = 3.1, CI = 1.1-8.3), and self-reported uncertainty about the results (OR = 1.8, CI = 1.1-2.7). These findings can help GC providers anticipate who might seek GC services and plan for clinical discussions of DTC-PGT results.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".