Utilization of Genetic Counseling after Direct‐to‐Consumer Genetic Testing: Findings from the Impact of Personal Genomics (PGen) Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it