International Coordination of Research Ethics Review: An Adequacy Model
Bibliographic record
Abstract
International direct-to-participant (DTP) genomics research involves the use of mobile technology to recruit, consent, and study participants remotely. This model can facilitate research across broad geographies and many countries, but must also comply with the norms of multiple recruitment jurisdictions, with each jurisdiction typically requiring at least one local research ethics review. Each additional research ethics review increases bureaucratic hurdles without necessarily strengthening the protection of participants’ rights and interests. For DTP genomic research, obtaining a review may in fact be impossible in the absence of a local research partner. This paper proposes an “adequacy” approach, inspired by data protection law, to coordinate the regulation and oversight of international DTP genomics research. This involves one country voluntarily assessing whether another country’s research ethics reviews are equivalent to its own, in terms of objectives and effectiveness. Ethics-approved projects led by researchers from countries recognized as adequate are deemed to comply with local norms, eliminating the need for a duplicative local review. Adequacy preserves the sovereignty of countries to determine their own regulatory aims and which other countries to trust. It therefore provides a voluntary, incremental path towards greater global coordination of health research oversight.
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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 | 0.321 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.048 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".