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Record W4288096068 · doi:10.1017/s0714980822000204

Life Satisfaction and Influenza Vaccination Among Older Adults in Canada

2022· article· en· W4288096068 on OpenAlexaffabout
Balanding Manneh, Melissa K. Andrew, Chidubem Ekpereamaka Okechukwu

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2022
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsDalhousie UniversityUniversity of British Columbia
Fundersnot available
KeywordsVaccinationMultinomial logistic regressionMedicineLogistic regressionGerontologyDemographyYoung adultEnvironmental healthImmunology

Abstract

fetched live from OpenAlex

Abstract Older adults have an increased risk of complications or death from influenza. Despite the benefits of vaccination for older adults, vaccination coverage among older adults ages 65 years and over is still below Canada’s national target of 80 per cent. As health–care-seeking behaviours are influenced by several factors, including life satisfaction, we investigated the relationship between life satisfaction and influenza vaccination among older adults. A sample (n = 22,424) from the 2015–2016 Canadian Community Health Survey data was analysed using descriptive and multinomial logistic regression analyses. Higher life satisfaction was associated with a more recent influenza vaccination history. Vaccination differed by gender, age, and self-reported health status, as women, much older adults, and those with the poorest health status were more likely to be vaccinated. The study suggests an association between life satisfaction and influenza vaccination. More research into the factors that impact influenza vaccination in older adults is needed to increase vaccination coverage in the older adult 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 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)0.003
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 score0.026
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.254
Teacher spread0.238 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissement→Same topicInfluenza Virus Research Studies→French-language works237,207→