Public health responses to West Nile virus : the role of risk perceptions and behavioral uncertainty in risk communication and policy
Bibliographic record
Abstract
Emerging and re-emerging infectious diseases provide a challenge to public health in that the frequency, location, duration, and severity of the disease and outbreak are not always readily identifiable. In the absence of such information, the need to understand what drives risk perceptions, risk trade-offs, and heterogeneity in population behaviors becomes important in designing effective and appropriate risk communications, public health messages, and interventions. In this thesis, four studies are described that examine risk perceptions, risk trade-offs, and behavioral uncertainties as they relate to West Nile virus (WNV) prevention and control strategies. In Chapter 2, the health belief model was used to examine the influence of health beliefs and demographics on health behaviors recommended to reduce the risk of WNV. Results showed that health beliefs and subsequent behaviors varied based on the perceived risk and disease context. Respondents were more likely to engage in recommended health behaviors if they received timely information, understood the benefits of a particular behavior, and lived in areas exposed to WNV. Chapter 3 explored behavioral and demographic risk factors associated with risk perceptions of WNV and WNV interventions. Unique associations were found which merit further study to understand the extent of their relationships. In Chapter 4, risk trade-offs of WNV interventions were examined between laypeople and health experts using multi-criteria decision analyses. Laypeople perceived some WNV interventions to be more effective than health experts reported them to be. Health experts were most concerned about the effectiveness of such interventions. This showed that laypeople were more willing to make risk trade-offs given the scenario. In Chapter 5, probabilistic modeling techniques were used to characterize variability and uncertainty in population, environmental, pesticide, and exposure characteristics. By modeling a realistic mosquito abatement campaign, we found that children under 6 are potentially at risk of exposure to malathion levels that exceed standards set by Canadian and US regulatory agencies. Together, these studies highlight the importance of targeted programs and risk communications to specific sub-populations bridging knowledge gaps. Though the findings are specific to WNV, their implications are far-reaching and useful in preparing for other emerging and re-emerging diseases.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".