Public Health Genomics (PHG): From Scientific Considerations to Ethical Integration
Bibliographic record
Abstract
Abstract Recent advances in our understanding of the human genome have raised high hopes for the creation of personalized medicine able to predict diseases well before they occur, or that will lead to individualized and therefore more effective treatments. This possibility of a more accurate science of the prevention and surveillance of disease also illuminates the field of public health, where the translation of genomic knowledge could provide tools enhancing the capacity of public health authorities to promote health and prevent diseases. But beyond scientific considerations, the use of genomics in public health research and interventions gives rise to several ethical and social issues of great importance. Considering the impact that PHG could have on the future of public health while still paying attention to the uncertainty surrounding the use of genomic databases for the benefit of populations, this article seeks to explore the promise of genomics in public health and the ethical issues that emerge from its application.
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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 | 0.135 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.086 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.030 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".