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Record W4415546499 · doi:10.1016/j.ypmed.2025.108435

Factors associated with human papillomavirus (HPV) non-vaccination among 14-year-old children in Canada

2025· article· en· W4415546499 on OpenAlexaffabout
Kristina Sabou, Anna-Maria Frescura, Gilla K. Shapiro, Marwa Ebrahim, Julie Laroche

Bibliographic record

VenuePreventive Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoPublic Health Agency of Canada
Fundersnot available
KeywordsHuman papillomavirusSocioeconomic statusLogistic regressionVaccinationMultivariate analysisImmunizationHPV vaccines

Abstract

fetched live from OpenAlex

Objective The purpose of this study is to identify factors associated with HPV non-vaccination among 14-year-olds in Canada. Methods This study employed data from the 2021 Childhood National Immunization Coverage Survey. Data were collected between January and June 2022 across Canada's 10 provinces and three territories. Multivariate logistic regression analysis was conducted, to identify factors linked to HPV non-vaccination among 14-year-olds. Results HPV non-vaccination was found to be independently and significantly associated with the child being born outside of Canada (aOR = 2.61, 95 % CI: 1.20,5.70) and having a history of parental refusal, reluctance, or delay of at least one routine childhood vaccine other than HPV vaccine for their child (aOR = 3.26, 95 % CI: 1.87,5.66). Socioeconomic status-related factors such as household income, parent/guardian education, and the child's visible minority status were not found to be associated with HPV non-vaccination. Conclusions Future research is needed to better understand the barriers to HPV vaccination among non-Canadian-born adolescents and to gain insight into the complex intersecting factors at the individual, interpersonal, organizational, and societal levels that contribute to HPV non-vaccination in this population.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.320
Teacher spread0.296 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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