Readiness of Community Members on Disaster Risk Reduction and Management
Bibliographic record
Abstract
Abstract: This study assessed the readiness of community members in a 5th-class municipality in Iloilo for Disaster Risk Reduction Management (DRRM) during the first quarter of 2024 as a basis for an action plan. Using a descriptive research design and a researcher-made survey validated through Carter V. Good and Douglas E. Scates' evaluation criteria, data were gathered from 379 community personnel. Reliability testing was conducted in Canlaon City Division to 30 personnel using Cronbach alpha formula. Descriptive and comparative analyses were conducted using frequency, percentage, mean, and the Mann-Whitney U test. Findings indicate that demographic factors such as age, sex, educational attainment, and income significantly influence readiness levels. Older individuals, females, and those with higher education and income levels exhibit greater preparedness in prevention and mitigation. While overall readiness across DRRM phases—prevention, mitigation, preparedness, response, and rehabilitation—is high, gaps exist in training participation, disaster communication, and management of records and aid. Notably, education and income impact prevention and preparedness but not response and rehabilitation, where older males demonstrate higher readiness. These results underscore the need for tailored strategies addressing demographic variations and enhancing training, communication, and management practices to improve community resilience. Keywords: Disaster risk reduction and management, readiness of community members
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".