A Tale of Two Capacities: Including Children and Decisionally Vulnerable Adults in Biomedical Research
Bibliographic record
Abstract
The participation of individuals who lack decision-making capacity is essential for advancing genomics research and neuroscience, but raises ethical and legal challenges relating to vulnerability, consent, and exclusion. Capacity differences between populations and individuals, the dynamics of capacity over time, and evolving legal consent and capacity regimes all raise uncertainty for researchers, institutional review boards, and policy makers. We review international ethical and legal best practices for including children and decisionally vulnerable adults in health research. Research ethics norms and literature tend to split such groups into narrow silos, which results in inconsistency and conceptual confusion, or to lump them together, which fails to take into account morally relevant differences. Through a narrative review of international norms, we identify challenges common to both groups, while drawing out distinctions reflecting their opposite capacity trajectories. Our comparison between these two populations clarifies underlying ethical concepts and offers opportunities for critique. Children need protection to foster their long-term autonomy, while decisionally vulnerable adults need to be provided with support in order to exercise their autonomy. This leads to differences in how researchers determine who lacks capacity, who has authority to consent, and what criteria guide such decision-making. We also consider how capacity issues color contemporary research governance debates over broad consent, data protection compliance, data sharing, and the return of individual research results and incidental findings.
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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 | 0.097 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.116 |
| Scholarly communication | 0.017 | 0.048 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".