Managing the Introduction of Biobanks to Potential Participants: Lessons from a Deliberative Public Forum
Bibliographic record
Abstract
Ongoing debate exists around how best to manage the issue of informed consent for research involving human tissue biobanks. However, the issue is well recognized and covered in the academic literature. A related and arguably equally important issue that to date has not received much attention is how best to manage the process of identifying and initially contacting individuals for their participation in a biobank. While many population-based biobanks strive for random sampling of healthy participants from the general population, disease-based biobanks usually need to rely on some sort of referral process to achieve specificity for type and subcategories of disease. There are thus numerous ethical implications regarding the way in which this referral process is managed. In this article we begin by providing a brief outline of the nature of the problems associated with the initial introduction between a biobank and potential research participants. We then consider data from a recent public deliberation on the topic of human tissue biobanking. In these discussions, participants were posed questions regarding their views pertaining to the introduction of potential donors to biobanks, and asked to make recommendations to be considered by policy makers in British Columbia, Canada. Based on these data we conclude that there is general agreement that introduction of research biobanks to potential donors should be conducted face to face, and by a medical professional known to the donor, and depending on donor circumstances, is acceptable during either pre- or postoperative periods. The strong preference for the introduction to involve a family physician should be considered in the future design of biobank contact and consent processes.
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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 | 0.422 | 0.448 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.044 | 0.065 |
| Scholarly communication | 0.044 | 0.060 |
| Open science | 0.012 | 0.044 |
| Research integrity | 0.040 | 0.045 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".