Beyond and within public engagement: a broadened approach to engagement in biobanking
Bibliographic record
Abstract
Social studies on biobanking have traditionally focused on public engagement, that is, engagement with donors, patients and the general public as an important factor of sustainability. In this article, we claim that, in order to fully understand the way biobanks work, it is necessary to pay attention to a number of other actors, which have an equal, if not greater, impact on their practices and strategies. This means taking a broadened approach to biobank engagement. By using data collected from interviews with different biobank experts based in five different countries (UK, Canada, Finland, Spain and Iceland), we identify seven communities, including the public, that emerge as relevant. Such relationships condition the way biobanks develop, act and plan. The discussion illustrates how the relationships with those seven communities are articulated. We conclude that there is a need for a broadened approach to biobank engagement in order to understand biobank sustainability.
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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 | 0.006 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".