“The risks cannot be compensated”: The willingness to donate DNA for science and its relationship with economic preferences
Bibliographic record
Abstract
Abstract The accumulation of large genetic data is crucial for the scientific advancement of genetic research and precision medicine, but various participation biases threaten the validity of genetic research data sets. To better understand the decision to participate and its relationship with economic incentives and preferences, we studied the stated willingness to donate DNA for science by saliva sample in a representative panel of Dutch households. There were two randomized treatments, varying (i) the information material on benefits and risks and (ii) the intended financial incentive. The first treatment had no detectable effect, suggesting insensitivity to the information material. The higher incentive conditions had modest and diminishing effects, suggesting that offering higher incentives is not cost‐effective. Stated reasons not to donate DNA concentrated on personal risks, e.g., privacy violations and data exploitation. Accordingly, stated risk willingness was found strongly associated, followed by trust and positive reciprocity. Revealed economic preferences were not associated. The results support previous findings for self‐rated health, interpersonal trust and confidence in science or societal institutions but not for certain demographic variables (e.g., age, education and religiosity). We conclude by proposing strategies to encourage participation, e.g., to reallocate resources to risk‐minimizing or compensatory measures.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".