Factors Associated with Increased Risk Perception of Pandemic Influenza in Australia
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".