Scientists’ perspectives on consent in the context of biobanking research
Bibliographic record
Abstract
Most bioethics studies have focused on capturing the views of patients and the general public on research ethics issues related to informed consent for biobanking and only a handful of studies have examined the perceptions of scientists. Capturing the opinions of scientists is important because they are intimately involved with biobanks as collectors and users of samples and health information. In this study, we performed interviews with scientists followed by qualitative analysis to capture the diversity of perspectives on informed consent. We found that the majority of scientists in our study reported their preference for a general consent approach although they do not believe there to be a consensus on consent type. Despite their overall desire for a general consent model, many reported several concerns including donors needing some form of assurance that nothing unethical will be done with their samples and information. Finally, scientists reported mixed opinions about incorporating exclusion clauses in informed consent as a means of limiting some types of contentious research as a mechanism to assure donors that their samples and information are being handled appropriately. This study is one of the first to capture the views of scientists on informed consent in biobanking. Future studies should attempt to generalize findings on the perspectives of different scientists on informed consent for biobanking.
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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 | 0.280 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.131 |
| Scholarly communication | 0.028 | 0.022 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.064 | 0.055 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".