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Record W4399355436 · doi:10.3389/fpubh.2024.1400782

Determinants of influenza non-vaccination among Canadian children: insights from a nationwide survey

2024· article· en· W4399355436 on OpenAlexafffundabout
Abdallah Alami, Sailly Dave, Caren Uhlik, Marwa Ebrahim, Daniel Krewski, Julie Laroche

Bibliographic record

VenueFrontiers in Public Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversity of OttawaPublic Health Agency of Canada
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsVaccinationMedicineLogistic regressionImmunizationPandemicDemographyInfluenza vaccineEducational attainmentEthnic groupSeasonal influenzaEnvironmental healthImmunologyCoronavirus disease 2019 (COVID-19)Disease

Abstract

fetched live from OpenAlex

Background To identify determinants influencing Canadian parents’ decision not to vaccinate their children aged 6 months to 17 years against seasonal influenza. Methods Data from the 2022 Childhood COVID-19 Immunization Coverage Survey, a national survey of approximately 10,500 Canadian parents/guardians and their children, was analyzed. The survey examined influenza vaccine coverage, parental perspectives on vaccines, reasons for hesitancy, and factors influencing immunization. Socio-demographic characteristics, including ethnicity, household income, working sector, educational attainment, and prevalence of chronic medical conditions among children were considered. Historical vaccine uptake and the impact of the COVID-19 pandemic on immunization decisions were also reviewed. Key determinants of non-vaccination in the 2021–2022 influenza season were analyzed using multivariable logistic regression, with a statistical significance level set at p -value <0·05. Results 70% of children aged 6 months to 17 years did not receive the seasonal influenza vaccine. Key predictors for non-vaccination included: residing in rural settings (aOR 1·35, 95% CI 1·13–1·60), parental education attainment of less than high school (aOR 2·48, 95% CI 1·24–4·97), and the absence of chronic medical conditions in children (aOR 1.60, 95% CI 1.34-1.91)· Other strong predictors included lower household income; deterrence due to the COVID-19 pandemic; and parental hesitancy stemming from concerns about the vaccine’s safety, effectiveness, and by beliefs that their child was not at risk of contracting the influenza or severe consequences from the infection. Conclusion This research underscores pivotal determinants of parental decisions not to vaccinate their children against seasonal influenza and sheds light on the impact of the COVID-19 pandemic. The results highlight the importance of addressing safety concerns and providing clear information to alleviate hesitancy.

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 score0.023
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.006
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.315
Teacher spread0.287 · 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

Citations5
Published2024
Admission routes3
Has abstractyes

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