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Record W4210424465 · doi:10.1051/e3sconf/202234005010

Psychological preparedness of coastal communities in Surabaya: a preliminary finding

2022· article· en· W4210424465 on OpenAlexaboutno aff
Listyati Setyo Palupi

Bibliographic record

VenueE3S Web of Conferences · 2022
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsPreparednessDisaster preparednessNatural disasterGeographyEmergency managementPopulationSituatedSocioeconomicsEnvironmental planningPolitical scienceSociologyDemography

Abstract

fetched live from OpenAlex

Indonesia has been acknowledged as one of countries that has the largest coastline in the world after Canada and Norway. Situated between Australia plate, Pacific plate, and Eurasia plate has made the country prone to disaster. Tsunami is one of the disasters that struck coastal communities in Indonesia. Tsunami is one type of disaster that bring devastated impact economically, socially and psychologically especially for the coastal communities including for Surabaya community that live along the coast. Studies found that psychologically preparing individual to face disaster could help to reduce the psychological impact of the disaster. Therefore, identifying the psychological preparedness is pertinent to reduce the risk of disaster especially for the coastal communities in Surabaya that were prone to tsunamis and other natural hazards. The purpose of the study is to describe the psychological preparedness for disaster of coastal communities in Surabaya. The result shows that the psychological preparedness for disaster of the participants was mostly in average level. Therefore, psychological preparedness for disaster needs to be improved especially among female with age between 12-35 years old population in order to reduce the risk of psychological impact of disaster.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.096
GPT teacher head0.414
Teacher spread0.318 · 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 designObservational
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

Citations3
Published2022
Admission routes1
Has abstractyes

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