Genomics and Public Health Research: Can the State Allow Access to Genomic Databases?
Bibliographic record
Abstract
Because many diseases are multifactorial disorders,the scientific progress in genomics and genetics should be taken into consideration in public health research. In this context, genomic databases will constitute an important source of information. Consequently, it is important to identify and characterize the State's role and authority on matters related to public health,in order to verify whether it has access to such databases while engaging in public health genomic research. We first consider the evolution of the concept of public health, as well as its core functions, using a comparative approach (e.g. WHO, PAHO, CDC and the Canadian province of Quebec). Following an analysis of relevant Quebec legislation, the precautionary principle is examined as a possible avenue to justify State access to and use of genomic databases for research purposes. Finally, we consider the Influenza pandemic plans developed by WHO, Canada, and Quebec,as examples of key tools framing public health decision-making process.We observed that State powers in public health, are not,in Quebec,well adapted to the expansion of genomics research.We propose that the scope of the concept of research in public health should be clear and include the following characteristics:a commitment to the health and well-being of the population and to their determinants; the inclusion of both applied research and basic research; and, an appropriate model of governance (authorization, follow-up,consent, etc.).We also suggest that the strategic approach version of the precautionary principle could guide collective choices in these matters.
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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 | 0.050 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.058 |
| Scholarly communication | 0.028 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".