Broad Consent for Future Research: International Perspectives
Bibliographic record
Abstract
Abstract In the United States, final amendments to the Federal Policy for the Protection of Human Subjects (“the Common Rule”) were published on January 19, 2017, and they will take effect on January 21, 2019. One of the most widely discussed provisions is that for the first time, federal regulations governing research with humans authorize the use of broad consent for future, unspecified research on individually identifiable biospecimens and associated data. Many questions have been raised about broad consent, including what effect it will have on research and whether it adequately protects the interests of research participants. There are lessons to be learned for the U.S. and other countries by looking to countries that already have experience with broad consent for biobank collection and with the storage and subsequent use of the biospecimens and data. This article describes how broad consent works in five countries—Canada (in Quebec), Israel, Nigeria, Taiwan, and the United Kingdom—and with different types of biobanks: national biobanks, federated biobanks, and regional biobanks. Evaluating the provisions and challenges of the broad consent approaches in these countries can inform policies for this increasingly used approach to biobank regulation.
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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 | 0.194 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.074 |
| Scholarly communication | 0.025 | 0.033 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.035 | 0.044 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".