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Record W2290322479 · doi:10.9778/cmajo.20140088

Trends in medical and nonmedical immunization exemptions to measles-containing vaccine in Ontario: an annual cross-sectional assessment of students from school years 2002/03 to 2012/13

2015· article· en· W2290322479 on OpenAlexaffvenueabout
Sarah Wilson, C. Y. Seo, Gillian Lim, J. Fediurek, Natasha S. Crowcroft, Shelley L. Deeks

Bibliographic record

VenueCMAJ Open · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsPublic Health Ontario
Fundersnot available
KeywordsMedicineMeaslesPoliomyelitisVaccinationPolio vaccineCohortDemographyImmunizationFamily medicinePediatricsLegislationDiphtheriaEnvironmental healthImmunologyLawPolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Under Ontario legislation, for select vaccine-preventable diseases nonimmunized or under-immunized students must undergo vaccination or provide a statement of exemption, or risk suspension from school. At the time of this assessment, these diseases included measles, mumps, rubella, diphtheria, tetanus and polio. METHODS: Exemptions data for the school years 2002/03 to 2012/13 were obtained from the Immunization Records Information System used in Ontario. Temporal trends were expressed for 7- and 17-year-old students by exemption classification (medical, prior immunity, religious or conscientious belief, total) at the provincial level, by school year and by birth cohort. Regional analysis was conducted for the 2012/13 school year. A temporal trend analysis of exemptions for measles-containing vaccines was performed by using a Poisson distribution with a 2-sided test (α = 5%). RESULTS: For both 7- and 17-year-old students, religious or conscientious exemptions for measles-containing vaccines significantly increased over the study period (p < 0.001 in both age groups), whereas medical exemptions decreased (p < 0.001 in both age groups). The trends were reproduced when examined by birth cohort. The percentage of Ontario students with any exemption classification (total exemptions) remained low (< 2.5%) during the study period, although considerable geographic variation was noted. INTERPRETATION: Ontario data suggest that nonmedical exemptions have increased during the last 11 years, consistent with trends reported elsewhere. The trend toward increasing religious or conscientious exemptions coupled with declining medical exemptions explains why total exemptions have remained stable or decreased at the provincial level. The prominent geographic variability in exemptions suggests that targeted interventions may be suitable for consideration.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.056
GPT teacher head0.429
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2015
Admission routes3
Has abstractyes

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