A blank check or a global public good? A qualitative study of how ethics review committee members in Colombia weigh the risks and benefits of broad consent for data and sample sharing during a pandemic
Bibliographic record
Abstract
Broad consent for future use facilitates the reuse of participant-level data and samples, which can conserve limited resources by confirming research findings and facilitate the development and evaluation of public health and clinical advances. Ethics review committees (ERCs) have to balance different stakeholder concerns when evaluating the risks and benefits associated with broad consent for future use. In this qualitative study, we evaluated ERC members' concerns about different aspects of broad consent, including appropriate governance, community engagement, evaluation of risks and benefits, and communication of broad consent for future use in Colombia, which does not currently have national guidance related to broad consent for future use. We conducted semi-structured, in-depth interviews with 24 ERC members from nine Colombian ERCs. We used thematic analysis to explore ERC members' concerns related to broad consent for future use. Most ERC members expressed concern about the idea of not specifying the purposes for which data would be used and by whom and suggested that pre-specifying governance procedures and structure would address some of their concerns about broad consent. ERC members emphasized the need for engaging communities and ensuring research participants understood broad consent for future use-related language in informed consent forms. Researchers and research institutions are under increasing pressure to share public health-related data. ERC members play a central role in balancing the priorities of different stakeholders and maintaining their community's trust in public health research. Further work is needed on guidelines for developing language around broad consent, evaluating community preferences related to data sharing, and developing standards for describing governance for data or sample sharing in the research protocol to address ERC members' concerns around broad consent for future use.
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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 | 0.033 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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; both teacher heads 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".