Characteristics of immunized and un-immunized students, including non-medical exemptions, in Ontario, Canada: 2016–2017 school year
Bibliographic record
Abstract
BACKGROUND: Our objectives were: (1) to quantify and describe un-immunized students in Ontario, Canada and assess the extent to which these students have exemptions; and (2) to quantify and describe students with non-medical exemptions (NMEs), including what proportion have up-to-date immunizations. METHODS: We examined Ontario students 7 to 17 years-of-age in the 2016-2017 school year using information within a centralized immunization repository. We identified and described students with different immunization/exemption classifications by age, sex, school type, geography and area-level material deprivation using descriptive and multivariable logistic regression analyses. Finally, we assessed the immunization status of students with NMEs, by antigen. RESULTS: We found that students could be recorded as un-immunized with or without an NME, or be immunized with an NME. From a cohort of 1.65 million students, 2.9% of students had zero vaccine doses recorded, and of these 68% had no exemption of any kind. A total of 2.4% of students had an NME. Of these, 39% were un-immunized and 61% had received ≥1 vaccine. Among all students with NMEs, 19-48% had up-to-date immunizations, varying by antigen. Factors associated with increased odds of having a NME and being un-immunized included: attendance at private and 'other' schools, rural residence, and geography. Older age and greater area-level deprivation were associated with a reduced odds. CONCLUSIONS: Our assessment revealed that Ontario students with NMEs cannot be assumed to be un-immunized and at risk for all vaccine-preventable diseases. Conversely, not all un-immunized students had NMEs suggesting that future studies of un-immunized children in Ontario must consider additional factors beyond NME status alone. Other jurisdictions that use NME data to inform research and surveillance of vaccine hesitancy and risks for VPD outbreaks may wish to undertake a similar assessment to determine how well student NMEs correlate with student immunization status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".