The Promotion of ‘Grab Bags’ as a Disaster Risk Reduction Strategy
Bibliographic record
Abstract
INTRODUCTION: An all-of-society approach to disaster risk reduction emphasizes inclusion and engagement in preparedness activities. A common recommendation is to promote household preparedness through the preparation of a 'grab bag' or 'disaster kit', that can be used to shelter-in-place or evacuate. However, there are knowledge gaps related to how this strategy is being used around the world as a disaster risk reduction strategy, and what evidence there is to support recommendations. METHODS: In this paper, we present an exploratory study undertaken to provide insight into how grab bag guidelines are used to promote preparedness in Canada, China, England, Japan, and Scotland, and supplemented by a literature review to understand existing evidence for this strategy. RESULTS: There are gaps in the literature regarding evidence on grab bag effectiveness. We also found variations in how grab bag guidelines are promoted across the five case studies. DISCUSSION: While there are clearly common items recommended for household grab bags (such as water and first aid kits), there are gaps in the literature regarding: 1) the evidence base to inform guidelines; 2) uptake of guidelines; and 3) to what extent grab bags reduce demands on essential services and improve disaster resilience.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".