Disaster Awareness and Risk Reduction Knowledge among School Students in Beemdatt Municipality, Nepal
Bibliographic record
Abstract
Natural disasters such as floods, earthquakes, landslides, and hailstorms have become more frequent and dangerous in recent years, especially in developing countries like Nepal. School students of Bheemdatt municipality are at high risk of natural disasters, especially when they are in school and at home. This study focuses on Bheemdatt Municipality of Kanchanpur District. The main objective of the study was to assess disaster awareness and risk reduction knowledge among school students in Bheemdatt Municipality, Nepal. The research was conducted using both primary and secondary sources of data. A Simple random sampling method was used to select respondents. Data were collected through surveys and interviews conducted in 14 schools of which six were government and eight were private. The sampled schools represent different levels of disaster risk. A total of 110 students participated in the study, along with teachers and school principals. The findings indicate that the majority of students had acquired knowledge about disasters mainly through school textbooks, while other sources such as television, radio, and the internet were reported to be less frequently utilized. Among the types of disasters experienced, earthquakes, floods, and hailstorms were the most common in the study area. Many students understood disaster preparedness and mitigation, some remained unclear about the specific actions required before, during, and after a disaster. The study finds that strengthening disaster education in schools helps students gain a clearer understanding of disaster risk reduction (DRR). It emphasizes the crucial role of youth education in safeguarding lives and communities. Additionally, the study examines the current status of disaster education in Nepal and offers recommendations to improve community resilience through educational initiatives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".