High rubella seronegativity in daycare educators.
Bibliographic record
Abstract
BACKGROUND: Congenital rubella syndrome, which is associated with severe malformations, can result from infants exposed in utero to maternal rubella infection. Health care workers and school-based educators are targeted for immunization, but evidence is scarce on rubella seronegativity in daycare centre educators who appear to be a high-risk occupational group. The purpose of the study was to generate new evidence on the magnitude of rubella seronegativity and associated risk factors in daycare centre educators. METHODS: Sera and questionnaires were collected between October and December 2001 from 481 female educators working in 81 daycare centres in Montréal, Québec. Rubella IgG serology was performed using ELISA. RESULTS: An overall seronegativity of 10.2% was found. The positive predictive value of previous rubella vaccination with seropositivity was high (92.1%). Ninety-one percent of the women were of childbearing age (= 49 years). Only 1.3% (n = 6) were currently pregnant, none of whom were seronegative. Significant predictors of seronegativity for educator- and daycare-level variables included lack of previous rubella vaccination (OR = 3.60; 95% CI: 1.43, 9.01), not having own children (OR = 3.76; 95% CI: 1.67, 8.55), age per 5-year increment (OR = 0.81; 95% CI: 0.66, 0.99), and increased number of colds in educators in the daycare centre in the last two weeks (OR = 1.15; 95% CI: 1.01, 1.31). INTERPRETATION: The high proportion of seronegativity, in addition to the potential increased transmission in daycare centres emphasize the need for a review of the rubella vaccination recommendations and health promotion interventions targeted to this occupational group.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".