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Record W4390344780 · doi:10.18280/jesa.560612

Assessing Trauma Healing Methods for Volcanic Disaster Evacuees in Indonesia

2023· article· fr· W4390344780 on OpenAlexvenueno aff
Fadly Usman, Jihan Kusuma Wardhani, Indah Cahyaning Sari, Saifuddin Chalim

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2023
Typearticle
Languagefr
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
Fundersnot available
KeywordsVolcanoMedicineGeographyMedical emergencySeismologyGeology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.124
GPT teacher head0.468
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal Européen des Systèmes AutomatisésSame topicDisaster Response and ManagementFrench-language works237,207