Fear-related behaviors in situations of mass threat
Bibliographic record
Abstract
This Disaster Health Briefing focuses on the work of an expanding team of researchers that is exploring the dynamics of fear-related behaviors in situations of mass threat. Fear-related behaviors are individual or collective behaviors and actions initiated in response to fear reactions that are triggered by a perceived threat or actual exposure to a potentially traumatizing event. Importantly, fear-related behaviors modulate the future risk of harm.Disaster case scenarios are presented to illustrate how fear-related behaviors operate when a potentially traumatic event threatens or endangers the physical and/or psychological health, wellbeing, and integrity of a population. Fear-related behaviors may exacerbate harm, leading to severe and sometimes deadly consequences as exemplified by the Ebola pandemic in West Africa. Alternatively, fear-related behaviors may be channeled in a constructive and life-saving manner to motivate protective behaviors that mitigate or prevent harm, depending upon the nature of the threat scenario that is confronting the population.The interaction between fear-related behaviors and a mass threat is related to the type, magnitude, and consequences of the population encounter with the threat or hazard. The expression of FRBs, ranging from risk exacerbation to risk reduction, is also influenced by such properties of the threat as predictability, familiarity, controllability, preventability, and intentionality.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".