Harmonised consent in international research consortia: an impossible dream?
Bibliographic record
Abstract
It is well recognised that it can be difficult for researchers to ensure that proper consent provisions are in place for their research project. If this can be difficult for those conducting a single site study, those difficulties can be compounded when that study becomes a member of an international scientific research consortium. These consortia bring together often diverse groups of researchers who, while working on a common topic, may represent different countries, cultures and scientific methodologies. Harmonising consent information and processes across these studies can be a complicated task. At the local level, participants need to be informed of the details of the study to which they are being recruited, and informed of the implications of the study’s inclusion in the consortium. Likewise, the international consortium needs to make certain that member studies have met appropriate consent requirements so that participants’ samples and data can be shared with and used by the consortium for agreed purposes. A considerable amount of time and effort is needed to ensure an international consortium is running consistently across its constituent parts. And as the consortium grows, so do the complexities of dealing with the different regulatory and cultural norms of its members, while staying within the organisational requirements of the consortium itself. But a level of consistency, in terms of consent to use of data and samples, must be achieved across the consortium members in order for its work to proceed while respecting the consent provisions agreed to by participants. The question of how to arrive at this benchmark is a vital one. This paper will present issues raised as a result of an examination of consent materials used by International Cancer Genome Consortium (ICGC) members.
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.
How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".