Bibliographic record
Abstract
Canada has been known to have one of the better vaccine safety surveillance capacities in the world, but in the early 2000s, it was noted there was still room for improvement. How has Canada done over the last decade and is there more to be done? Canada has done well. First, there has been significant progress made by the Vaccine Vigilance Working Group to enhance the passive vaccine safety monitoring system and address potential issues arising from the review of surveillance data and cases or clusters of concern. Second, there has been an increased investigative capacity for clusters of adverse events and other vaccine safety issues, including an assessment and referral system for individuals with adverse events following immunizations (AEFIs). Third, the use of the Brighton Collaboration definitions and other international standards has facilitated international collaboration and represents the best standard of practice. Despite all these improvements, however, there is more that could be done. The sensitivity of Canada's passive surveillance system still varies from one province and territory to another. The timeliness of the data exchange flow could improve. The AEFI Signal Response Protocol, which identifies the processes and required actions for timely management of any newly detected or emerging vaccine safety signals, is a critical piece of a robust vaccine safety system but it is still in the making. It is commendable that Canada has decided to expand its focus on evaluation research from influenza vaccines to vaccine-preventable diseases more broadly, with the establishment of the Canadian Immunization Research Network (CIRN). CIRN's newly developed Provincial Collaborative Network and the move toward record linkages is excellent. These new investments are welcome in light of the rich vaccine development pipeline, the increased pool of available vaccines, and the growing set of technologies for vaccines production, delivery, and safety monitoring. What would round this all out would be a stronger capacity to monitor the implementation of vaccination programs and vaccine coverage, and better documentation of the reduction of the disease burden attributable to vaccination programs. Canada's investment in vaccines for the health of all deserves no less.
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 | 0.026 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| 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".