MétaCan
Menu
Back to cohort
Record W2292014527 · doi:10.1080/21645515.2016.1153207

Estimates and determinants of HPV non-vaccination and vaccine refusal in girls 12 to 14 y of age in Canada: Results from the Childhood National Immunization Coverage Survey, 2013

2016· article· en· W2292014527 on OpenAlexaffabout
Nicolas L. Gilbert, Heather Gilmour, Ève Dubé, Sarah Wilson, Julie Laroche

Bibliographic record

VenueHuman Vaccines & Immunotherapeutics · 2016
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsPublic Health OntarioUniversity of TorontoInstitut National de Santé Publique du QuébecInstitute for Clinical Evaluative SciencesStatistics CanadaPublic Health Agency of CanadaToronto Public HealthUniversité de Montréal
Fundersnot available
KeywordsVaccinationMedicineImmunizationLogistic regressionDemographyConfoundingEnvironmental healthImmunology

Abstract

fetched live from OpenAlex

Since the introduction of HPV vaccination programs in Canada in 2007, coverage has been below public health goals in many provinces and territories. This analysis investigated the determinants of HPV non-vaccination and vaccine refusal. Data from the Childhood National Immunization Coverage Survey (CNICS) 2013 were used to estimate the prevalence of HPV non-vaccination and parental vaccine refusal in girls aged 12-14 years, for Canada and the provinces and territories. Multivariate logistic regression was used to examine factors associated with non-vaccination and vaccine refusal, after adjusting for potential confounders. An estimated 27.7% of 12-14 y old girls had not been vaccinated against HPV, and 14.4% of parents reported refusing the vaccine. The magnitude of non-vaccination and vaccine refusal varied by province or territory and also by responding parent's country of birth. In addition, higher education was associated with a higher risk of refusal of the HPV vaccine. Rates of HPV non-vaccination and of refusal of the HPV vaccine differ and are influenced by different variables. These findings warrant further investigation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.485
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

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

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 head0.034
GPT teacher head0.318
Teacher spread0.284 · 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 teacher head, 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

Citations36
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueHuman Vaccines & ImmunotherapeuticsSame topicCervical Cancer and HPV ResearchFrench-language works237,207