H1N1 Was Not All That Scary: Uncertainty and Stressor Appraisals Predict Anxiety Related to a Coming Viral Threat
Bibliographic record
Abstract
H1N1 reached pandemic proportions in 2009, yet considerable ambivalence was apparent concerning the threat presented and the inclination to be vaccinated. The present investigation assessed several factors, notably appraisals of the threat, intolerance of uncertainty, and familiarity with the virus, that might contribute to reactions to a potential future viral threat. Canadian adults (N = 316) provided with several scenarios regarding viral threats reported moderate feelings of anxiety, irrespective of whether the viral threat was one that was familiar versus one that was entirely unfamiliar to them (H1N1 recurrence, H5N1, a fictitious virus: D3N4). Participants appraised the stressfulness of the threats to be moderate and believed that they would have control in this situation. However, among individuals with high intolerance of uncertainty, the viral threat was accompanied by high levels of anxiety, which was mediated by aspects of appraisals, particularly control and stressfulness. In addition, among those individuals that generally appraised ambiguous life events as being stressful, the viral threat appraisals were accompanied by still greater anxiety. Given the limited response to potential viral threats, these results raise concerns that the public may be hesitant to heed recommendations should another pandemic occur.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".