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Record W2953321025 · doi:10.82308/4796

Parents' human papillomavirus vaccine decision-making: theory, measurement and models

2017· article· en· W2953321025 on OpenAlexaboutno aff
Samara Perez

Bibliographic record

VenueeScholarship@McGill (McGill) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
Fundersnot available
KeywordsGenital wartsVaccinationGardasilMedicineImmunizationHPV vaccinesFamily medicineHuman papillomavirusCervixCervical cancerEnvironmental healthHPV infectionImmunologyCancerInternal medicine

Abstract

fetched live from OpenAlex

The human papillomavirus (HPV) infects approximately 550,000 Canadians annually. Cancers of the cervix, mouth, genitals, anus, head and neck are caused by various strains of the HPV. The HPV also causes genital warts. The disease and economic burden of HPV infections is high. Three HPV vaccines are available: Cervarix®, Gardasil®, and Gardasil ® 9. Consistent with global practices in developed countries, these vaccines are currently publicly funded for girls and provided in school-based programs in all provinces and territories in Canada. As of September 2016, six provinces provide publicly funded school-based programs for boys. Despite well-documented vaccine efficacy and effectiveness with minimal adverse effects, uptake of the HPV vaccines remains suboptimal in most countries, including Canada. Although HPV immunization rates have increased over the last decade, they remain significantly below the rates of other vaccine-preventable diseases. One of the main challenges for boys' uptake has been to help parents understand that the HPV vaccine is now available, recommended and effective for boys in reducing health risks for themselves and transmission to their partners. With low HPV uptake rates in Canada, success of increased vaccination rates is contingent on parents' awareness, understanding and ultimately their decision-making process. Of the HPV vaccination research that has targeted parents of boys, most studies examined demographic and descriptive factors associated with vaccination intentions. While this research is informative, it treats decision-making as binary, when there are likely multiple stages of vaccination decision-making. Conceptualizing vaccine decision-making as distinct stages would allow us to examine those individuals who are vaccine hesitant, as well as parents who are not yet aware or engaged in HPV vaccine decision-making. Moreover, much of the existing research on the correlates and factors associated with vaccination intentions are unreliable, which is likely due to differences in the conceptualization of the factors and inconsistent and unstable measures. This in turn provides limited insight about leverage points of how to move individuals along the HPV vaccine decision-making trajectory and ultimately increase HPV vaccine uptake.This dissertation addresses some of these research gaps by using theory-based research, as well as the development of two psychometrically validated scales, an extended HPV and HPV vaccine knowledge scale and the HPV Attitudes and Beliefs Scale (HABS) to identify the factors that are associated with HPV vaccination decision-making among a nationally representative sample of Canadian parents of 9-16-year-old boys using a longitudinal design. The unique contributions of the four manuscripts in this thesis are that by conceptualizing HPV vaccine decision-making as a series of distinct stages, by using theory, psychometrically-tested and validated measures, as well as multinomial logistic regression models, we can have a greater understanding about what influences parents' HPV vaccine decision-making for their sons. This more nuanced understanding will help to better target our efforts to increase HPV vaccine uptake for boys. Future research directions and recommendations for better informed and targeted interventions are made.

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)0.013
metaresearch head score (Gemma)0.049
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 score0.016
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.053
GPT teacher head0.313
Teacher spread0.260 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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