Danger Appraisals as Prospective Predictors of Disgust and Avoidance of Contaminants
Bibliographic record
Abstract
Although several cognitive theories have proposed specific types of appraisals hypothesized to increase fear and avoidance of contaminants, little research has tested these ideas. The current study utilized a prospective design to assess appraisals and dispositional traits in a normal sample several days prior to a behavioral approach task (BAT) involving commonly-encountered contamination stimuli. Danger appraisals significantly predicted behavioral avoidance and self-reported disgust, but not anxiety, during the BAT, even after accounting for neuroticism, disgust sensitivity, and subclinical obsessive-compulsive symptoms. The prospective design of the study establishes temporal precedence of danger appraisals, assessed during a period of low emotion, predicting subsequent emotional and behavioral response. Results also point to the importance of disgust sensitivity and the experience of disgust in response to everyday contaminants. These findings are discussed in light of public health outbreaks including Severe acute respiratory Syndrome (SARS) and the H1N1 flu, which have caused novel contamination threats worldwide in recent years.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".