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Record W2416160427

Trends in influenza vaccination in Canada, 1996/1997 to 2005.

2007· article· en· W2416160427 on OpenAlexaffabout
Jeff Kwong, Laura C. Rosella, Helen Johansen

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsInstitute for Clinical Evaluative Sciences
Fundersnot available
KeywordsVaccinationMedicinePopulation healthDemographyLogistic regressionOddsPopulationCommunity healthImmunizationEnvironmental healthPublic healthVirologyImmunology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: This article reports recent trends in influenza vaccination rates in Canada, provides data on predictors of vaccination in Canada for 2005, and examines longer-term effects of Ontario's universal influenza immunization program on vaccine uptake. DATA SOURCES: Data are from the 1996/1997 National Population Health Survey (NPHS) and the 2000/2001, 2003, and 2005 Canadian Community Health Survey (CCHS). ANALYTICAL TECHNIQUES: NPHS and CCHS data were used to estimate influenza vaccination rates of the population aged 12 or older. The Z test was used to assess differences between surveys, and the chi-squared test for trend was used to examine trends over time. Logistic regression was used to identify predictors of vaccination and to compare the odds of being vaccinated in Ontario versus other provinces. MAIN RESULTS: Nationally, influenza vaccination rates rose from 15% in 1996/1997 to 27% in 2000/2001, stabilized between 2000/2001 and 2003, and increased further to 34% by 2005. Vaccination rates for most high-risk groups still fall short of national targets. Ontarians continue to be more likely to be vaccinated than are residents of any other province, while residents of two of the territories--Nunavut and the Northwest Territories--are even more likely to be vaccinated than are Ontarians.

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)0.001
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 score0.461
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.070
GPT teacher head0.348
Teacher spread0.278 · 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

Citations67
Published2007
Admission routes2
Has abstractyes

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