Canadian vaccine research networks: Vaccine safety resources for Canada
Bibliographic record
Abstract
The Public Health Agency of Canada / Canadian Institutes of Health Research Influenza Research Network (PCIRN), established in 2009 to undertake evaluative research to inform public health decision making in Canada, is now being replaced by the Canadian Immunization Research Network (CIRN), which will retain the mandate of PCIRN but expand it to all vaccines including influenza vaccine. CIRN is organized as a network of networks focusing on undertaking research in the areas of vaccine safety, adverse events following immunization (AEFIs), vaccine hesitancy, vaccine effectiveness, and vaccine coverage. CIRN's networks include: a clinical trial network; a laboratory network; a modelling and economics network; a network of social science and humanities researchers; a vaccine safety surveillance network; a hospital-based surveillance network; a clinic network to evaluate serious AEFIs; and a network that links vaccine research capacity in provincial health agencies and departments. PCIRN has contributed to Canada's vaccine safety surveillance system and has facilitated the translation of safety research into policy. Vaccine safety surveillance and research will remain a focus of the newly formed Canadian Immunization Research Network.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.135 | 0.026 |
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".