A Qualitative Study Exploring the Consumer Experience of Receiving Self-Initiated Polygenic Risk Scores from a Third-Party Website
Bibliographic record
Abstract
ABSTRACT The number of people accessing their own polygenic risk scores (PRSs) online is rapidly increasing, yet little is known about why people are doing this, how they react to the information, and what they do with it. We conducted a qualitative interview-based study with people who pursued PRSs through Impute.me, to explore their motivations for seeking PRS information, their emotional reactions, and actions taken in response to their results. Using interpretive description, we developed a theoretical model describing the experience of receiving PRSs in a direct-to-consumer (DTC) context. Dissatisfaction with healthcare was an important motivator for seeking PRS information. Participants described having medical concerns dismissed, and experiencing medical distrust, which drove them to self-advocate for their health, which in turn ultimately led them to seek PRSs. Polygenic risk scores were often empowering for participants, but could be distressing when PRS information did not align with participants’ perceptions of their personal or family histories. Behavioural changes made in response to PRS results included dietary modifications, changes in vitamin supplementation and talk-based therapy. Our data provides the first qualitative insight into how people’s lived experience influence their interactions with DTC PRSs.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".