Association between Disaster Knowledge Level and the First Step of Stockpiling Food for a Disaster
Bibliographic record
Abstract
This study clarifies the association between disaster knowledge levels and beginning to stockpile food at home as a disaster preparedness. This survey was conducted between 18-20 December 2019 using a self-administered web-based questionnaire. The participants were recruited from panel members of an online survey company. A total of 1,200 adults living in the five Japanese prefectures with the highest predicted food shortages after the anticipated Nankai Trough earthquake, took part in the study. Multivariable logistic regression analyses revealed a significant positive relationship between disaster knowledge level and beginning food stockpiling (p for trend <0.001). Compared with those who had a low level of disaster knowledge, participants who had a medium level of knowledge were 2.11 times more likely to begin stockpiling food (adjusted odds ratio [OR]: 2.11, 95% confidence intervals [CI]: 1.49-2.97), whereas those with the highest knowledge level were 2.52 times more likely to begin stockpiling food (adjusted OR: 2.52, 95% CI: 1.79-3.56). Beginning food stockpiling can be the first step toward disaster preparedness. It is considered that people with low disaster knowledge levels are more likely to have no beginning food stockpiling and are at high risk for disasters. These findings suggest ways to approach prioritizing people facing high disaster risk.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".