The public’s acceptance of novel vaccines during a pandemic: a focus group study and its application to influenza H1N1
Bibliographic record
Abstract
As influenza H1N1 spreads around the world, health officials are considering the development and use of a new vaccine to protect the public and help control the outbreak. Acceptance of novel vaccines during health crises, however, is influenced by perceptions of a range of risks, including the risk of infection, risk of becoming severely ill or dying if infected, as well as the risk of serious side and long-term effects of the vaccine. A study on 11 focus groups was conducted with the public in Vancouver, Canada in 2006 and 2007 to explore how people assess these risks and how these assessments relate to their willingness to use novel vaccines in a pandemic. Concerns about using new vaccines during a pandemic differ from concerns about using established products in a non-crisis situation. Participants were hesitant to use novel vaccines because of a low perception of the early risk of infection in a pandemic, coupled with the many uncertainties that surround new vaccines and the emerging infectious disease, and owing to the concern that unsafe pharmaceuticals may be rushed to market during a health crisis. Understanding the public’s assessment of the risks related to, and willingness to use, novel vaccines during a pandemic can help officials promote disease-control measures in ways that improve the likelihood of acceptance by the public and may increase uptake of an H1N1 vaccine.(Published:26 March 2010)Citation:Emerging Health Threats Journal 2009, 2:e8.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".