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Record W4400391155 · doi:10.52609/jmlph.v4i3.133

A Systematic Review of the Efficacy of Full-Scale Simulation Exercises in Enhancing Hospital Disaster Preparedness

2024· review· en· W4400391155 on OpenAlexvenueno aff
Jameel Abualenain, Raghad Alhajaji, Loui Alsulimani

Bibliographic record

VenueThe Journal of Medicine Law & Public Health · 2024
Typereview
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPreparednessEmergency managementScale (ratio)Systematic reviewObservational studyDebriefingComputer scienceMedicineMedical educationMEDLINE

Abstract

fetched live from OpenAlex

Introduction: A full-scale simulation exercise is a comprehensive drill designed to replicate a real-world emergency scenario, thereby identifying the strengths and weaknesses in current practices. The overarching goal is to enhance healthcare system resilience through improved protocols, as highlighted in this systematic review tailored for researchers. The study aims specifically to assess the impact of full-scale simulations on enhancing hospital disaster plans. Methodology: Following PRISMA guidelines, this systematic review investigates the impact of full-scale simulation exercises on hospital disaster preparedness. The focus was on hospital staff involved in disaster and emergency preparedness training. The primary intervention was the execution of full-scale simulation exercises, and our research included various study designs, including randomised controlled trials and observational study designs. A comprehensive electronic database search was conducted, spanning PubMed, Scopus, Web of Science, and the Cochrane Central Register of Controlled Trials, from inception until October 9, 2023. The risk of bias was assessed using NIH tools. Results: The literature search yielded 2398 results, with 28 publications finally included in the systematic review. Summarising a broad range of disaster preparedness simulation exercises with a specific focus on full-scale simulation (FSS), the studies consistently demonstrated a positive impact on participants' skills, as well as identifying safety issues in hospital settings. Moreover, they revealed that simulations effectively addressed crucial areas for improvement in disaster response, including communication breakdowns, equipment deficiencies, and flaws in emergency plans. The studies utilised a multidimensional approach to evaluation metrics, encompassing non-technical skills, communication, teamwork, decision-making, and operational readiness. The exercises varied in duration from 30 minutes to multi-day simulations, covering a diverse range of disaster scenarios, such as mass casualties, viral epidemics, large aviation accidents, and terrorist attacks. Conclusion: Full-scale simulation exercises are a preparatory learning tool to test facility and staff readiness for complex emergencies. This systematic review focused on the different exercise scenarios used to address critical aspects of disaster response such as communication breakdowns, equipment deficiencies, and flaws in emergency plans. The scenarios and their duration were varied, and involved a multidimensional approach to evaluation. Such exercises enhance critical thinking and problem-solving skills, increase familiarity with potential emergencies, and build confidence in making judgments in real-world situations. In our opinion, there is a need for further programs that align simulation exercises with community resources for better preparedness in the face of public health 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 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)0.023
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.141
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.095
GPT teacher head0.479
Teacher spread0.384 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations10
Published2024
Admission routes1
Has abstractyes

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