The Decision to Vaccinate or Not during the H1N1 Pandemic: Selecting the Lesser of Two Evils?
Bibliographic record
Abstract
BACKGROUND: With the release of the H1N1 vaccine, there was much controversy surrounding its use despite strong encouragements to be vaccinated in the media. Though studies have examined factors influencing people's decision to be vaccinated, few have focused on how general beliefs about the world or where an individual gathers information might influence that decision. METHODOLOGY/PRINCIPAL FINDINGS: A cross-sectional web-based survey (N = 817) was conducted during the H1N1 outbreak after the vaccine was available. Variables examined included sociodemographic information, health related behaviours, specific beliefs concerning the H1N1 virus and its vaccine, as well as general beliefs, such as fear of contamination, intolerance of uncertainty, emotional states, coping behaviour, and the source of information concerning the virus. Three converging statistical methods were used to examine the associations - analysis of variance, logistic regression, and recursive partition modelling. The most consistent and strongest association was that negative beliefs about the H1N1 vaccine (e.g. fear of its side effects) was related to the decision not to be vaccinated, whereas beliefs about the dangers of the H1N1 virus was related to the decision to be vaccinated. Most notably, having very strong negative beliefs about the vaccine was a more powerful predictor than even strong beliefs about the dangers of the H1N1 virus. Furthermore, obtaining information from the Internet, as compared to more traditional sources of information (e.g., TV, newspapers) was related to the decision not to be vaccinated. CONCLUSIONS/SIGNIFICANCE: These results are consistent with the Health Belief Model. Importantly they suggest that during future pandemics public health officials should not only discuss the dangers of the pandemic but also (i) take additional steps to reassure the public about the safety of vaccines and (ii) monitor the information disseminated over the Internet rather than strictly relying on the more traditional mass media.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".