Methods used for immunization coverage assessment in Canada, a Canadian Immunization Research Network (CIRN) study
Bibliographic record
Abstract
Accurate and complete immunization data are necessary to assess vaccine coverage, safety and effectiveness. Across Canada, different methods and data sources are used to assess vaccine coverage, but these have not been systematically described. Our primary objective was to examine and describe the methods used to determine immunization coverage in Canada. The secondary objective was to compare routine infant and childhood coverage estimates derived from the Canadian 2013 Childhood National Immunization Coverage Survey (cNICS) with estimates collected from provinces and territories (P/Ts). We collected information from key informants regarding their provincial, territorial or federal methods for assessing immunization coverage. We also collected P/T coverage estimates for select antigens and birth cohorts to determine absolute differences between these and estimates from cNICS. Twenty-six individuals across 16 public health organizations participated between April and August 2015. Coverage surveys are conducted regularly for toddlers in Quebec and in one health authority in British Columbia. Across P/Ts, different methodologies for measuring coverage are used (e.g., valid doses, grace periods). Most P/Ts, except Ontario, measure up-to-date (UTD) coverage and 4 P/Ts also assess on-time coverage. The degree of concordance between P/T and cNICS coverage estimates varied by jurisdiction, antigen and age group. In addition to differences in the data sources and processes used for coverage assessment, there are also differences between Canadian P/Ts in the methods used for calculating immunization coverage. Comparisons between P/T and cNICS estimates leave remaining questions about the proportion of children fully vaccinated in Canada.
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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 | 0.032 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".