A Psychosocial Risk Assessment and Management Framework to Enhance Response to CBRN Terrorism Threats and Attacks
Bibliographic record
Abstract
Evidence in the disaster mental health literature indicates that psychosocial consequences of terrorism are a critical component of chemical, biological, radiological, and nuclear (CBRN) events, both at the clinical level and the normal behavioral and emotional levels. Planning for such psychosocial aspects should be an integral part of emergency preparedness. As Canada and other countries build the capacity to prevent, mitigate, and manage CBRN threats and events, it is important to recognize the range of social, psychological, emotional, spiritual, behavioral, and cognitive factors that may affect victims and their families, communities, children, the elderly, responders, decision makers, and others at all phases of terrorism, from threat to post-impact recovery. A structured process to assist CBRN emergency planners, decision makers, and responders in identifying psychosocial risks, vulnerable populations, resources, and interventions at various phases of a CBRN event to limit negative psychosocial impacts and promote resilience and adaptive responses is the essence of our psychosocial risk assessment and management (P-RAM) framework. This article presents the evidence base and conceptual underpinnings of the framework, the principles underlying its design, its key elements, and its use in the development of decision tools for responders, planners, decision makers, and the general public to better assess and manage psychosocial aspects of CBRN threats or attacks.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".