Determinants of Bacillus Calmette–Guérin (BCG) vaccination among Québec children
Bibliographic record
Abstract
OBJECTIVES: To identify determinants of Bacillus Calmette-Guérin (BCG) vaccination among children born in Québec, Canada, in 1974, the last year of the systematic vaccination campaign. METHOD: A retrospective birth cohort was assembled in 2011 through probabilistic linkage of administrative databases (n=81,496). Potential determinants were documented from administrative databases and by interviewing a subset of subjects (n=1643) in 2012. Analyses were conducted among subjects with complete data, 71,658 (88%) birth cohort subjects and 1154 (70%) interviewed subjects, then redone using multiple imputation. Determinants of BCG vaccination during the organized vaccination program (in 1974), and after the program (1975 onwards) were assessed separately. Logistic regression with backward elimination was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Overall, 46% of subjects were BCG vaccinated, 43% during the program and 4% after it ended. BCG vaccination during the program was associated with parents' birthplace and urban or rural residence. BCG vaccination after the organized program was only related to ethnocultural origin of the child's grandparents. CONCLUSION: Different factors were related to vaccination within and after the organized program. Determinants of BCG vaccination in Québec, Canada, have never been studied and will be useful for future research and vaccination campaigns.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".