The effect of genetic test‐based risk information on behavioral outcomes: A critical examination of failed trials and a call to action
Bibliographic record
Abstract
Encouraging individuals at risk for common complex disease like heart disease, cancer, and diabetes to adopt lifestyle changes (e.g., smoking cessation, exercise, proper nutrition, increased screening) could be powerful public health tools to decrease the enormous personal and economic burden of these conditions. Theoretically, genetic risk information appears to be a compelling tool that could be used to provoke at-risk individuals to adopt these lifestyle changes. Unfortunately, however, numerous studies now have shown that providing individuals with genetic test-based risk information has little to no impact on their behavior. In this article (a commentary not a systematic review), the failed trials in which genetic information has been used as a tool to induce behavior change will be critically examined in order to identify new and potentially more effective ways forward.
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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 | 0.367 | 0.662 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".