Disaster and emergency response simulation education for medical students: A Scoping Review Protocol

Protocol registration

The review protocol follows the JBI guidance for scoping reviews (Peters et al., 2022) and is reported in line with the Preferred Reporting Items for Systematic Reviews and Meta- Analyses extensions for Scoping Reviews (PRISMA-ScR) and Protocols (Shamseer et al., 2015; Tricco et al., 2018). The protocol is registered on Open Science Framework (OSF; osf.io/wra5g. ; https://doi.org/10.17605/OSF.IO/WRA5G).

Eligibility criteria

We will include any study design (e.g., observational studies, quantitative studies, qualitative studies, mixed methods studies) describing simulation methodologies for teaching medical students concepts relevant to disaster or emergency response (concept). Studies will be included which focus on instruction of medical students (participants) in a medical training environment (e.g. university or medical school, nationally or internationally recognised disaster response or public health entities) (context). Studies must be published between 2001–2025. This timeframe will ensure that current practice and methodologies are reflected and furthermore considers the significant paradigm shift in disaster preparedness after the 2001 terrorist attacks in the United States (Gowing et al., 2017) as well as lessons learned from COVID-19 response. Given advanced translational capacity via online platforms like Google translate, language will not be restricted. Inclusion and exclusion criteria are detailed in Table 1.

Information sources

The search strategy, developed with an information specialist, will be applied to PubMed, Embase, SCOPUS, and ERIC. Grey literature will be reviewed via Google Scholar, utilising the first 400 hits (Haddaway et al., 2015), and the Open Access Thesis and Dissertations (OATD) (available at: https://oatd.org/, accessed 15 April 2025). The reference list of all included documents will be screened for additional studies using citationchaser, supplemented by hand-searching reference lists where necessary (Haddaway et al., 2021). The full search strategy for each database is detailed in Appendix A and are available on OSF.

Data management and study selection

Study records will be imported into EndNote and uploaded to Covidence. Duplicates will be removed within Covidence. Titles and abstracts will be screened independently by pairs of reviewers (each record is screened by at least two people) for assessment against the inclusion criteria for the review. Disagreements will be resolved by a third reviewer. Prior to title and abstract screening, a pilot screening of 10% of documents will be carried out between the 2–3 reviewers who will review each title and abstract independently to ensure consistency of the application of the inclusion/exclusion criteria. A PRISMA flow diagram will map the number of records identified, included, and excluded. Full texts will also be screened independently by pairs of reviewers (each record is screened by at least two people) and reasons for exclusion at full text stage will be recorded and reported in the final review.

Data extraction and synthesis

The data extraction table will be piloted by two independent reviewers using a subset of included studies (e.g., 10% of eligible studies, selected to represent all study designs and data types). This will ensure that fields capture the review objectives correctly and consistently (Garritty et al., 2024). Data extraction will be treated as an iterative process as suggested in literature (Pollock et al., 2023), and, as such, a wider team discussion will be conducted following the pilot to discuss the quality and applicability of the data extraction process. Based on feedback, the data extraction tool will be amended as needed before proceeding with the full data extraction. Data extraction, using the tool (Table 2), will be carried out in pairs (one person to extract and one to check). Extracted data will include basic demographic data (e.g. country, year) data on context and participants (e.g. what kind of educational institution, what level of medical student), and concept (e.g. what kind of simulation exercise, fidelity level, what kind of disaster was simulated). Data on researcher-reported challenges, facilitators, and future suggestions will also be extracted. All reviewers will pilot the data extraction tool to ensure consistency of application and will operate under the supervision of the two primary reviewers.

Given the nature of scoping review data extraction, we will target information presented in the methods, results, and discussion/findings sections and/or the abstract sections of included resources. However, as needed, we will extract data from supplementary materials, appendices, and communication with authors. (Pollock et al., 2023; Thomas & Harden, 2008).

Risk of bias: Given the stated aim of this research as well as the methodological recommendation that risk of bias assessment is not undertaken for scoping reviews, a risk of bias assessment will not be conducted (Peters et al., 2022)

Data analysis and presentation

Descriptive statistics, tables, and figures will be used as appropriate to describe the articles included. This description will include the methodologies used for simulation exercises to instruct medical students in disaster/emergency preparedness and response, facilitators/challenges for these education methods, and any information about perceived success. Basic qualitative content analysis will be conducted to identify and describe information about disaster education simulation methodologies and barriers/facilitators therein (Pollock et al., 2023). The analysis will highlight the reported key features, strengths, and limitations of the identified methodologies, as described by the study authors. Within the purview of a scoping review, this analysis will include the quantification of text and frequency counts of discrete extraction items (e.g. number of studies using a specific method, where this method falls on predetermined scales of simulation complexity (low-fidelity vs high-fidelity) (Cowling et al., 2021), frequency of stated challenges/facilitators, and, if appropriate, geographic distribution of said frequencies.

As this is a scoping review, analysis and presentation will not cover effectiveness, meaningfulness, or accuracy of the described methodologies and associated challenges/facilitators (Pollock et al., 2023).