Psychosocial Management Before, During, and After Emergencies and Disasters—Results from the Kobe Expert Meeting
Bibliographic record
Abstract
Emergencies and disasters typically affect entire communities, cause substantial losses and disruption, and result in a significant and persistent mental health burden. There is currently a paucity of evidence on safe and effective individual- and community-level strategies for improving mental health before, during, and after such events. In October 2018, the World Health Organization (WHO) Centre for Health Development (WHO Kobe Centre) convened a meeting bringing together leading Asia Pacific and international disaster research experts. The expert meeting identified key research needs in five major areas, one being "Psychosocial management before, during, and after emergencies and disasters". Experts for this research area identified critical gaps in observational research (i.e., the monitoring of long-term psychological consequences) and interventional research (i.e., the development and evaluation of individual- and community-level interventions). Three key research issues were identified. First, experts underscored the need for a standardized and psychometrically robust instrument that classified the mental health/psychosocial risk of people within both a clinical and community setting. Then, the need for a standardization of methods for prevention, screening, diagnosis, and treatment for affected people was highlighted. Finally, experts called for a better identification of before, during, and after emergency or disaster assets associated with greater community resilience.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".