Framing Genomics, Public Health Research and Policy: Points to Consider
Bibliographic record
Abstract
Genetic information can be used to target interventions that improve health and prevent disease. Indeed, the results of population genomics research could be useful for public health and national pandemic plans. Yet, firm scientific evidence originating from such research and the indicators of the role of health determinants, gene-gene and gene-environment interaction remain to be assessed and validated before being integrated into pandemic plans or public health programmes. It is not clear what is the role of the State in research on the elucidation of the determinants of gene-gene and gene-environment interactions and how, when, and if such data can be accessed and used for such planning. Over a period of 3 years, we sought to address these questions by gathering data and literature relevant to research in public health genomics, preparing issues papers and, finally, consulting with stakeholders on a provisional 'points to consider' document at various times. Examining in turn the issues of privacy, State powers, stakeholder perceptions, and public participation, we propose in this article, for each of these themes, a series of recommendations aiming to provide guidance on the role of the State in the use of genomic information for public health research, prevention and planning.
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 | 0.211 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.024 | 0.114 |
| Scholarly communication | 0.054 | 0.061 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.077 | 0.052 |
| Insufficient payload (model declined to judge) | 0.005 | 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".