Barriers and Opportunities in Consent and Access Procedures in Low- and Middle-Income Country Biobanks: Meeting Notes from the BCNet Training and General Assembly
Bibliographic record
Abstract
As biobanking research in low- and middle-income countries (LMICs) continues to grow, novel legal and policy considerations have arisen. Also, while an expansive literature has developed around these issues, the views and concerns of individual researchers in these contexts have been less actively studied. These meeting notes aim to contribute to the growing literature on biobanking in LMICs by communicating a number of challenges and opportunities identified by biobank researchers themselves. Specifically, we describe concerns that emerge in consent and access policy domains. First, we present a review of the literature on distinct policy and legal concerns faced in LMICs, giving special attention to the general absence of practitioner perspectives. From there, we outline and discuss considerations that were raised by meeting participants at a Biobank and Cohort Building Network (BCNet) Ethical, Legal, and Social Issues training program. We conclude by proposing that the unique perspectives of biobank researchers in LMICs should be given serious attention and further research on these perspectives should be conducted.
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 | 0.002 | 0.007 |
| 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 | 0.000 |
| 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".