Consumer Perceptions of Interactions With Primary Care Providers After Direct-to-Consumer Personal Genomic Testing
Bibliographic record
Abstract
BACKGROUND: Direct-to-consumer (DTC) personal genomic testing (PGT) allows individuals to learn about their genetic makeup without going through a physician, but some consumers share their results with their primary care provider (PCP). OBJECTIVE: To describe the characteristics and perceptions of DTC PGT consumers who discuss their results with their PCP. DESIGN: Longitudinal, prospective cohort study. SETTING: Online survey before and 6 months after results. PARTICIPANTS: DTC PGT consumers. MEASUREMENTS: Consumer satisfaction with the DTC PGT experience; whether and, if so, how many results could be used to improve health; how many results were not understood; and beliefs about the PCP's understanding of genetics. Participants were asked with whom they had discussed their results. Genetic reports were linked to survey responses. RESULTS: Among 1026 respondents, 63% planned to share their results with a PCP. At 6-month follow-up, 27% reported having done so, and 8% reported sharing with another health care provider only. Common reasons for not sharing results with a health care provider were that the results were not important enough (40%) or that the participant did not have time to do so (37%). Among participants who discussed results with their PCP, 35% were very satisfied with the encounter, and 18% were not at all satisfied. Frequently identified themes in participant descriptions of these encounters were actionability of the results or use in care (32%), PCP engagement or interest (25%), and lack of PCP engagement or interest (22%). LIMITATION: Participants may not be representative of all DTC PGT consumers. CONCLUSION: A comprehensive picture of DTC PGT consumers who shared their results with a health care provider is presented. The proportion that shares results is expected to increase with time after testing as consumers find opportunities for discussion at later appointments or if results become relevant as medical needs evolve. PRIMARY FUNDING SOURCE: National Institutes of Health.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".