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Record W3165443003 · doi:10.18295/squmj.5.2021.074

Nursing Students’ Perceived Disaster Preparedness and Response

2021· article· en· W3165443003 on OpenAlexaff
Joy Kabasindi Kamanyire, Ronald Wesonga, Susan Achora, Anju Malik, Sultan Al‐Shaqsi, Jamila AS Alhabsi

Bibliographic record

VenueSultan Qaboos University medical journal · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity of Toronto
FundersSultan Qaboos University
KeywordsMedicinePreparednessDisaster preparednessDisaster responseDisaster planningNursingMedical emergencyEmergency managementHuman factors and ergonomicsPoison control

Abstract

fetched live from OpenAlex

OBJECTIVES: This study aimed to assess the perceived level of disaster preparedness and response among nursing students in Oman, as the country is disaster prone and experiences disasters every three to five years. METHODS: A descriptive cross-sectional pilot study was conducted from May to June 2019 using a stratified cluster sampling method among nursing students recruited from two government nursing colleges in Muscat, Oman. The Disaster Preparedness Evaluation Tool and Disaster Response Self-Efficacy Scale were used to assess the knowledge, skill, disaster management and self-efficacy in handling disasters. RESULTS: A total of 51 students participated in this study. Most students (78.4%) had experienced a disaster while at home. Overall, the students had moderate levels of knowledge (3.17 ± 1.49), skill (3.12 ± 1.52), post-disaster management (3.22 ± 1.44) and self efficacy in responding to disaster (2.93 ± 1.16). CONCLUSION: Nursing students in Oman have experienced disasters and are willing to respond when called upon though they possess moderate knowledge and confidence in handling disasters.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.390
Teacher spread0.361 · 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

Citations19
Published2021
Admission routes1
Has abstractyes

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