Consent recommendations for research and international data sharing involving persons with dementia
Bibliographic record
Abstract
Consent is generally required for research and sharing rich individual-level data but presents additional ethical and legal challenges where participants have diminished decision-making capacity. We formed a multi-disciplinary team to develop best practices for consent in data-intensive dementia research. We recommend that consent processes for research and data sharing support decision-making by persons with dementia, protect them from exploitation, and promote the common good. Broad consent designed to endure beyond a loss of capacity and combined with ongoing oversight can best achieve these goals. Persons with dementia should be supported to make decisions and enabled to express their will and preferences about participation in advance of a loss of capacity. Regulatory frameworks should clarify who can act as a representative for research decisions. By promoting harmonization of consent practices across institutions, sectors, and countries, we hope to facilitate data sharing to accelerate progress in dementia research, care, and prevention.
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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 | 0.277 | 0.457 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.034 | 0.029 |
| Insufficient payload (model declined to judge) | 0.047 | 0.026 |
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".