The impact of multiplex genetic testing on disease risk perceptions
Bibliographic record
Abstract
This study assessed the effects of multiplex genetic testing on disease risk perceptions among 216 healthy adults. Participants, aged 25-40, were recruited through the Multiplex Initiative, which offered a genetic susceptibility test for eight common diseases. Participants completed baseline telephone and web-based surveys prior to making the testing decision. Three months after the receipt of mailed test results, participants completed a follow-up telephone survey. Risk perceptions for the eight diseases were measured at baseline and follow-up, along with beliefs about genetic causation of those diseases. The main results were: (i) mean risk perceptions were considerably stable from baseline to follow-up; (ii) the best predictors of follow-up risk perceptions were the corresponding baseline perceptions and family history; and (iii) within-individuals, most participants increased or decreased their risk perceptions for specific diseases in concordance with the number of risk markers they carry, their family history and their beliefs about genetic causality of diseases. In conclusion, participants presented a vigilant approach to the interpretation of genetic test results, which provides reassurance with regard to a potential inflation of risk perceptions in the population because of multiplex genetic testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".