Code Orange: A Systematic Review of Psychosocial Disaster Response
Bibliographic record
Abstract
Introduction: Survivors of mass casualty incidents are vulnerable to both physical and psychological injuries. Hospitals need to triage the walking wounded victims, their loved ones, and witnesses for symptoms of emotional distress to ensure that those who are traumatized benefit from proactive psychological treatment. Hospitals must also manage the influx of searching family and friends, and be able to reunite them with their loved ones, to reduce chaos and prevent hospital skipping. Aim: To analyze previous research on institutional psychosocial disaster response, what has or has not worked, and lessons learned in order to develop evidence-based future planning suggestions. Methods: A literature search was conducted on the following electronic databases: (Medline 2007 to July 2018), (Embase 2007 to July 2018), (PsycInfo 2007 to July 2018). A combination of subject headings and free text keywords were used to perform the searches. After removing duplicates, abstracts were screened independently by two reviewers for the following inclusion criteria: 1) crisis intervention (in a disaster situation), 2) mention of psychosocial response or lack thereof and lessons learned, 3)relevant outcomes, 4) OECD countries, and 5) journal articles published 2007–Present. Review articles were excluded. Primary and secondary reviewers are in the process of discussing discrepancies. Data extraction will be conducted from all articles that meet the inclusion criteria. Key themes to be analyzed include psychological casualties, searching family and friends, and family reunification plans. Results: The initial search yielded 6,267 results. 5,294 articles remained after duplicates were removed. Of the 4,890 reviewed thus far, 269 articles met inclusion criteria. Discussion: Although a wealth of existing literature notes the need for an effective psychosocial response in mass trauma and disaster situations, no prior study has analyzed the efficacy of such interventions or laid out an evidence-based plan. This study will fill this much-needed gap in the literature.
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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 | 0.028 | 0.097 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.037 | 0.036 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".