Ethical Considerations in Biobanks: How a Public Health Ethics Perspective Sheds New Light on Old Controversies
Bibliographic record
Abstract
Biobanks, collections of biospecimens with or without linked medical data, have increased dramatically in number in the last two decades. Their potential power to identify the underlying mechanisms of both rare and common disease has catalyzed their proliferation in the academic, medical, and private sectors. Despite demonstrated public support of biobanks, some within the academic, governmental, and public realms have also expressed cautions associated with the ethical, legal, and social (ELSI) implications of biobanks. These issues include concerns related to the privacy and confidentiality of data; return of results and incidental findings to participants; data sharing and secondary use of samples; informed consent mechanisms; ownership of specimens; and benefit sharing (i.e., the distribution of financial or other assets that result from the research). Such apprehensions become amplified as more researchers seek to pursue national and cross-border collaborations between biobanks. This paper provides an overview of two of the most contentious topics in biobank literature - informed consent and return of individual research results or incidental findings - and explores how a public health ethics lens may help to shed new light on how these issues may be best approached and managed. Doing so also demonstrates the important role that genetic counselors can play in the ongoing discussion of ethically appropriate biobank recruitment and management strategies, as well as identifies important areas of ongoing empirical research on these unresolved topics.
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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 | 0.196 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.022 | 0.159 |
| Scholarly communication | 0.040 | 0.033 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.033 | 0.042 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".