Assessing Trauma Healing Methods for Volcanic Disaster Evacuees in Indonesia
Bibliographic record
Abstract
This study aims to evaluate the effectiveness of various trauma healing methods implemented in the Pronojiwo District of Lumajang Regency, East Java Province, Indonesia.This region has been selected due to the recurring eruptions of Mount Semeru over the past three years, starting in 2020.While the government has focused on evacuation strategies, shelter location, and infrastructure-related activities, this study concentrates on initiatives aimed at enhancing the mental health of evacuees housed in temporary shelters.Various trauma-healing techniques were employed, including motivational talks, games, educational activities, and singing.The research involved 2,489 refugees displaced by the Semeru eruption in December 2022, and a quantitative method was adopted with a sample of 215 respondents, comprising 105 children and 110 adults.The findings indicated that the respondents held diverse perspectives on the traumahealing methods provided.The specific adversities each respondent faced following the disaster significantly influenced their perceptions.Among children up to adolescence, activities such as games, singing, and cooperative learning were generally well-received.However, methods involving talks, storytelling, motivation, and profound communication were found to potentially exert a positive impact on adult to elderly respondents.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".