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Record W3142498133 · doi:10.35790/jkp.v7i1.25205

PENGARUH PENDIDIKAN KEBENCANAAN BANJIR BANDANG TERHADAP KESIAPSIAGAAN MAHASISWA PROGRAM STUDI ILMU KEPERAWATAN FK UNSRAT

2019· article· en· W3142498133 on OpenAlexaff
M. Andhy Nurmansyah, Andi Buanasasi

Bibliographic record

VenueJurnal Keperawatan/Jurnal Keperawatan · 2019
Typearticle
Languageen
FieldComputer Science
TopicEdcuational Technology Systems
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsFlash floodPreparednessRespondentFlood mythPsychologyEmergency managementGeographyMedical educationPolitical scienceMedicineArchaeology

Abstract

fetched live from OpenAlex

Abstract: Flash flood is one of the most frequent disasters in Indonesia. The city of Manado was included in the event of the worst banjir bandang disaster. Preparedness greatly impacts on disasters that occur. Given the sudden onset of disaster and detrimental to many aspects, preparedness is needed to anticipate disasters. Therefore we need emergency education and simulation training to improve preparedness. Location of research for students who live in prone to flash flood disasters. The aim was to determine the effect of flash flood disaster education on the preparedness of nursing science students in FK Unsrat. The research design was using a nonrandomized control group pretest - posttest design. Samples were 32 people using consecutive sampling technique. Methods of collecting data using knowledge and attitude questionnaires, early warning systems, emergency response plans and resource mobilization to measure respondent preparedness and statistical tests using the Wilcoxon test and Man Whitney test. The results of the study obtained a P-Value of 0,000 (≤ α = 0.05) which means that there is a significant difference. The conclusions of the results of this study indicate an increase in preparedness in nursing students after being given counseling and emergency simulation training.Keywords: Disaster Education, Flash Flood, PreparednessAbstrak: Banjir bandang adalah salah satu bencana yang paling sering terjadi di Indonesia. Kota Manado masuk dalam peristiwa bencana banjir bandang terparah. Kesiapsiagaan sangat berdampak terhadap bencana yang terjadi. Mengingat bencana yang datangnya secara mendadak dan merugikan banyak aspek, kesiapsiagaan sangat diperlukan untuk mengantisipasi bencana. Oleh sebab itu diperlukan pendidikan kebencanaan dan pelatihan simulasi darurat untuk meningkatkan kesiapsiagaan. Lokasi tempat penelitian pada mahasiswa yang tinggal di daerah rawan bencana banjir bandang. Tujuan untuk mengetahui pengaruh pendidikan kebencanaan banjr bandang terhadap kesiapsiagaan mahasiswa program studi ilmu keperawatan FK Unsrat. Metode penelitian yaitu menggunakan desain nonrandomized control group pretest – posttest design. Sampel berjumlah 32 orang menggunakan teknik consecutive sampling. Metode pengumpulan data menggunakan kuesioner pengetahuan dan sikap, sistem peringatan dini, rencana tanggap darurat dan mobilisasi sumber daya untuk mengukur kesiapsiagaan responden dan uji statistik menggunakan uji Wilcoxon dan uji Man Whitney. Hasil penelitian di dapat nilai P – Value sebesar 0,000 (≤ α =0,05) yang berarti ada perbedaan yang signifikan. Simpulan hasil penelitian ini menunjukkan adanya peningkatan kesiapsiagaan pada mahasiswa keperawatan setelah diberikan penyuluhan dan pelatihan simulasi darurat.Kata kunci : Pendidikan Kebencanaan, Banjir Bandang, Kesiapsiagaan

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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 head0.011
GPT teacher head0.258
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2019
Admission routes1
Has abstractyes

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