Public Health Genomics (PHG) and Public Participation: Points to Consider
Bibliographic record
Abstract
Large-scale population biobanks, which aim to collect biological tissues, personal health information, and genomic data, are being introduced worldwide with the promise of increasing knowledge on chronic diseases such as diabetes and heart disease. Experts recognize the need for public participation to address the many social, legal and ethical complexities raised by the introduction of biobanks for public health research. However many researchers and decision makers struggle with how to promote public participation. This paper presents six issues that public participation must address. These issues are then applied to three large scale genetic biobank projects: CARTaGENE, Generation Scotland, and the United Kingdom Biobank. Finally, the efforts of these biobanks will be compared to the British Columbia Biobank deliberation project, which implemented a deliberative public participation experiment on biobanking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.121 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.069 |
| Scholarly communication | 0.028 | 0.028 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.031 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 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".