Immunisation registers in Canada: progress made, current situation, and challenges for the future
Bibliographic record
Abstract
Immunisation registers have the capacity to capture data on the administration of vaccine doses at the individual level within the population and represent an important tool in assessing immunisation coverage and vaccine uptake. In 1999, the National Advisory Committee on Immunization recommended that a network of immunisation registers be established in Canada. The Canadian Immunization Registry Network (CIRN) was established to coordinate the development of standards and facilitate the sharing of knowledge and experience to develop a national network of such registers. In 2003, the National Immunization Strategy identified immunisation registers as an important component in improving national immunisation surveillance. In addition, there has been consistent public and professional interest in a national immunisation register being available and considerable progress has been made in developing technologies to facilitate the capture of immunisation-related data. More specifically, the automated identification of vaccines, through the use of barcodes on vaccines, will facilitate collection of data related to administered vaccine doses. Nevertheless, challenges remain in the implementation of immunisation registers in all Canadian provinces and territories such that Canada still does not currently have a fully functional network of immunisation registers with the capacity to be interoperable between jurisdictions and to allow for data to be captured at the national level.
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 | 0.032 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".