A Psychosocial Risk Assessment and Management Framework to Enhance Response to CBRN Terrorism Threats and Attacks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it