Ethical implications of including children in a large biobank for genetic‐epidemiologic research: A qualitative study of public opinion
Bibliographic record
Abstract
The National Institutes of Health and other federal agencies are considering initiating a cohort study of 500,000 people, including 120,000 children, to measure genetic and environmental influences on common diseases. A community engagement pilot study was conducted to identify public attitudes and concerns about the proposed cohort study, including the ethics of involving children. The pilot included 15 focus groups where the inclusion of children in the proposed cohort study was discussed. Focus groups, conducted in six cities, included 141 adults of different ages, incomes, genders, ethnicities, and races. Many of the concerns expressed by participants mirrored those addressed in pediatric research guidelines. These concerns included minimizing children's fear, pain, and burdens; whether to include young children; and how to obtain children's assent. There was little agreement about which children can assent. Some voiced concern about children's privacy, but most expected that parents would have access to children's study results. Some believed children would not benefit from participating, while others identified personal and societal benefits that might accrue. A few people believed that children's participation would not advance the study's goals. To successfully include children, proposed cohort study would need to address children's changing capabilities and rights as they grow and reach the age of consent.
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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 | 0.123 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.031 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".