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Record W1981055588 · doi:10.1155/2010/947906

Factors Associated with Increased Risk Perception of Pandemic Influenza in Australia

2010· article· en· W1981055588 on OpenAlexaboutno aff
Jennifer Jacobs, Melanie Taylor, Kingsley Agho, Garry Stevens, Margo Barr, Beverley Raphael

Bibliographic record

VenueInfluenza Research and Treatment · 2010
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicQuarter (Canadian coin)DemographyPopulationMedicinePerceptionInfluenza pandemicMultivariate analysisGerontologyRisk perceptionEnvironmental healthPsychologyCoronavirus disease 2019 (COVID-19)GeographyDiseaseInfectious disease (medical specialty)Sociology

Abstract

fetched live from OpenAlex

The aim of this study was to assess factors associated with increased risk perception of pandemic influenza in Australia. The sample consisted of 2081 Australian adults aged 16 years and older who completed a short three item pandemic influenza question module which was incorporated into the NSW Health Adult Population Health Survey during the first quarter of 2007. After adjusting for covariates, multivariate analysis indicated that those living in rural regions were significantly more likely to perceive a high risk that a pandemic influenza would occur, while those with poor self-rated health perceived both a high likelihood of pandemic and high concern that self/family would be directly affected were such an event to occur. Those who spoke a language other than English at home and those on low incomes and younger people (16-24 years) were significantly more likely to have changed the way they lived their lives due to the possibility of pandemic influenza, compared to those who spoke only English at home, middle-high income earners, and older age groups, respectively. This data provides an Australian population baseline against which the risk perceptions of demographic subgroups regarding the current, and potential future pandemics, can be compared and monitored.

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)0.002
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 score0.013
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.319
GPT teacher head0.473
Teacher spread0.154 · 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

Citations42
Published2010
Admission routes1
Has abstractyes

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