Canada: Public Health Genomics
Bibliographic record
Abstract
Canada has a diverse population of 32 million people and a universal, publicly funded health care system provided through provincial and territorial health insurance plans. Public health activities are resourced at provincial/territorial level with strategic coordination from national bodies. Canada has one of the longest-standing genetics professional specialty organizations and is one of the few countries offering master's level training designed specifically for genetic counselors. Prenatal screening is offered as part of routine clinical prenatal services with variable uptake. Surveillance of the effect of prenatal screening and diagnosis on the birth prevalence of congenital anomalies is limited by gaps and variations in surveillance systems. Newborn screening programs vary between provinces and territories in terms of organization and conditions screened for. The last decade has witnessed a four-fold increase in requests for genetic testing, especially for late onset diseases. Tests are performed in provincial laboratories or outside Canada. There is wide variation in participation in laboratory quality assurance schemes, and there are few regulatory frameworks in Canada that are directly relevant to genetics testing services or population genetics. Health technology assessment in Canada is conducted by a diverse range of organizations, several of which have produced reports related to genetics. Several large-scale population cohort studies are underway or planned, with initiatives to harmonize their conduct and the management of ethical issues, both within Canada and with similar projects in other countries.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.134 | 0.022 |
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".