ArticleBMC emergency medicine2025
Post-earthquake emergency nurse allocation: a human resource management approach based on simulation modeling.
Article in BMC emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Building Resilience in Emergency Care Teams: A Narrative Review of Team-Based and Organisational Strategies for Nurses and Allied Health Professionals.Journal of multidisciplinary healthcare · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aims to develop a mathematical model to estimate the number of emergency nurses required to ensure the continuity of healthcare services in the aftermath of an earthquake.
backgroundThe February 6, 2023, Kahramanmaraş earthquake, which severely impacted southeastern Turkey, caused widespread devastation in Hatay province. The destruction of infrastructure, along with the large number of casualties and injuries, created an overwhelming demand for medical services, putting an extraordinary strain on the region’s healthcare system. Hatay, with limited medical resources and a single operating hospital, faced significant challenges in maintaining adequate care for the injured. This situation underscores the critical need for effective planning and resource allocation, particularly in estimating the number of emergency nurses required to manage the immediate aftermath of such disasters.
methodsThe study focused on Hatay Mustafa Kemal University Hospital, the sole healthcare provider in Hatay, following the 2023 Kahramanmaraş earthquakes. Using real field data (e.g., building collapse rates, injury estimations, and population distribution), a simulation was conducted. The M/M/s queuing theory model was applied to calculate the number of nurses required, factoring in patient arrival rates, nurse care capacity, and working shifts.
resultsBased on an estimated 11,645 injured patients over 144 h, the model concluded that 27 nurses per shift (totaling 81 nurses for 24-hour care) would be necessary to sustain full-capacity service. The projections closely aligned with actual hospital data.
conclusionThe model provides a scalable, scenario-sensitive planning tool that can support emergency preparedness efforts. Its basis in real disaster data enhances both its reliability and applicability, positioning it as a practical decision-making aid for policymakers and hospital administrators aiming to strengthen disaster organizational resilience through evidence-based nurse staffing strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.