Public deliberation to develop ethical norms and inform policy for biobanks: Lessons learnt and challenges remaining
Bibliographic record
Abstract
Public participation is increasingly an aspect of policy development in many areas, and the governance of biomedical research is no exception. There are good reasons for this: biomedical research relies on public funding; it relies on biological samples and information from large numbers of patients and healthy individuals; and the outcomes of biomedical research are dramatically and irrevocably changing our society. There is thus arguably a democratic imperative for including public values in strategic decisions about the governance of biomedical research. However, it is not immediately clear how this might best be achieved. While different approaches have been proposed and trialled, we focus here on the use of public deliberation as a mechanism to develop input for policy on biomedical research. We begin by explaining the rationale for conducting public deliberation in biomedical research. We focus, in particular, on the ELS (ethical, legal, social) aspects of human tissue biobanking. The last few years have seen the development of methods for conducting public deliberation on these issues in several jurisdictions, for the purpose of incorporating lay public voices in biobanking policy. We explain the theoretical foundation underlying the notion of deliberation, and outline the main lessons and capacities that have been developed in the area of conducting public deliberation on biobanks. We next provide an analysis of the theoretical and practical challenges that we feel still need to be addressed for the use of public deliberation to guide ethical norms and governance of biomedical research. We examine the issues of: (i) linking the outcomes of deliberation to tangible action; (ii) the mandate under which a deliberation is conducted; (iii) the relative weight that should be accorded to a public deliberative forum vs other relevant voices; (iv) evaluating the quality of deliberation; and (5) the problem of scalability of minipublics.
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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 | 0.361 | 0.299 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.015 | 0.082 |
| Scholarly communication | 0.048 | 0.062 |
| Open science | 0.010 | 0.028 |
| Research integrity | 0.025 | 0.035 |
| Insufficient payload (model declined to judge) | 0.009 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".