Consent recommendations for research and international data sharing involving persons with dementia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it