“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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".