Pengaruh Penggunaan Panduan Tanggap Bencana Terhadap Strategi Koping Keluarga Dalam Menghadapi Kerentanan Bencana Tsunami Di Desa Gunturharjo Kabupaten Wonogiri
Bibliographic record
Abstract
Background: Tsunamis are one of the most frequent disasters in Indonesia and are very risky along the southern coast of the island of Java, including the area of Wonogiri Regency which has a southern coastline. The tsunami resulted in losses not only of loss of property and lives but also of the psychosocial impact of mental health disorders. Therefore tsunami disaster preparedness is carried out not only on physical aspects but also on psychosocial aspects such as disaster response by improving family coping strategies. Methods: This study wanted to see the effect of using disaster response guidebooks on family coping strategies to deal with tsunamis. The population of this study was people living in the coastal area of Nampu, Guntur Harjo sub-district, Parang Gupito, Wonogiri Regency, Central Java, with a sample of 240 households. The study design used one group pre-post test with a quasi-experimental approach where the research data collected were analyzed using a Chi-Square test. Result: The results showed that p-Value = 0,000 <0,005 so it can be concluded that there are differences in family coping strategies between before and after using the tsunami disaster response guidelines. Conclusion: It is recommended to continue preparedness efforts and disaster response efforts using existing guidelines and conduct research on areas that have different characteristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".