Curriculum Design in Disaster Medicine and Mass Casualty Incident Training: A Scoping Review of Healthcare Education

Curriculum Design in Disaster Medicine and Mass Casualty Incident Training: A Scoping Review of Healthcare Education | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Curriculum Design in Disaster Medicine and Mass Casualty Incident Training: A Scoping Review of Healthcare Education Naglaa Mohamed Abdelhamied, Abu Omayer, Salma Abdalla Elmisbah, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9230758/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background Responding effectively to mass casualty incidents (MCIs) depends on well-trained healthcare professionals. However, there is no comprehensive synthesis that systematically maps how disaster and MCI training curricula are created, implemented, and assessed across various healthcare roles worldwide. Methods We performed a scoping review guided by the Arksey and O'Malley framework and adhered to PRISMA-ScR guidelines. Seven databases: PubMed, Embase, Scopus, PsycINFO, CINAHL, Cochrane Library, and ClinicalTrials.gov were searched for English-language studies published from 2015 to 2025. Two reviewers independently screened articles and extracted data. We mapped training outcomes using Moore's Expanded Outcomes Framework (Levels 1–7) and categorized studies based on curriculum architecture type. Additionally, we developed three original analytical constructs: a Curriculum Architecture Typology, a Curricular Coherence Analysis, and a Cognitive Scaffolding Taxonomy. Results Forty-one studies met the inclusion criteria, representing various geographical regions, learner groups, and study designs. The curricula fell into four main types: Stand-Alone (n = 14), Embedded (n = 16), Longitudinal (n = 4), and Model Proposal (n = 7). Evaluation of outcomes showed a Moore Ceiling: 53.7% of studies measured satisfaction (L2), but only 12.2% evaluated performance outcomes (L5), and none achieved patient or community health outcomes (L6/L7). Most studies focused solely on factual knowledge (16 studies) and did not assess procedural knowledge (8 studies), highlighting a Knowledge Depth Gap. Interprofessional curricula were uncommon, with only 6 of 41 studies (14.6%) explicitly involving interprofessional learner groups, despite MCIs requiring coordinated team responses. Longitudinal curricula reached deeper Moore outcome levels more often than stand-alone formats. Conclusion This review highlights significant gaps in outcomes depth, interprofessional integration, and curricular coherence across the field. Future curricula should focus on longitudinal structures, intentional interprofessional design, more comprehensive outcome measurement, and systematic cognitive scaffolding to improve disaster preparedness education. Mass casualty incident Disaster medicine Curriculum design Healthcare education Scoping review Moore's outcomes framework Interprofessional education Medical education Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background When a sudden-impact disaster strikes, emergency medical services are often overwhelmed as the number and severity of casualties surpass available resources. This causes systemic strain and creates an urgent need for coordinated action. Disasters, whether natural or human-made, such as terrorism, transportation accidents, fires, or extreme weather, can lead to these mass casualty incidents (MCIs). While many multi-casualty events involve large numbers of patients, they are usually manageable with current resources; however, if conditions worsen and resources are depleted, these incidents can escalate into full-scale MCIs, overwhelming response systems [ 1 ]. Effective MCI response relies on coordinated actions starting with scene recognition and continuing through quick triage and resource deployment to minimize preventable deaths and disabilities. To enhance preparedness, the World Health Organization (WHO) has provided guidance for healthcare providers and institutions dealing with MCI situations [ 2 ]. MCIs encompass various categories that mirror the range of threats. Planned mass gatherings, such as major sporting events, as well as typical periodic incidents like transportation accidents, fires, and severe weather, each require specific planning. Additionally, incidents involving hazardous exposures to chemical, biological, or radiological agents form another category. Nuclear-related events, whether accidental, intentional, or acts of terrorism, are also significant. The most critical are catastrophic public health emergencies, including nuclear detonations, large-scale explosions, major hurricanes, and widespread outbreaks, which pose the greatest challenges for MCI response efforts [ 3 ]. Response begins when the first emergency personnel at the scene identify an MCI. Handling these incidents involves a structured approach that typically consists of five key components: ensuring scene safety, conducting a scene assessment, transmitting information to incident command, preparing the site for casualties, and applying the START (Simple Triage and Rapid Treatment) protocol. This framework helps create order amid chaos and serves as the foundation for disaster response training [ 3 ]. Disasters, armed conflicts, and pandemics are happening more frequently, increasing the importance of MCI (Mass Casualty Incident) preparedness across healthcare systems [ 4 ]. The success of humanitarian and emergency responses depends heavily on strong collaboration within and across different professional and disciplinary groups. Over the years, global crises have influenced how organizations train and prepare their personnel [ 5 ]. The International Committee of the Red Cross (ICRC) began offering humanitarian health courses in the 1970s, followed soon after by Médecins Sans Frontières (MSF). As the scale and complexity of disasters grew, universities began including humanitarian health education in the 1990s. The September 11 attacks shifted healthcare preparedness to a national security priority, leading to increased federal funding in the USA and a stronger focus on public health infrastructure and hospital readiness for large-scale emergencies [ 6 , 7 ]. Emergency preparedness involves four key phases. According to the Emergency Management Accreditation Program (EMAP), these are mitigation (reducing hazard risks and severity), preparedness (planning to enhance capabilities and capacity), response (ensuring safe and efficient operations during a disaster), and recovery (supporting restoration and rehabilitation afterward) [ 8 ]. Despite these frameworks, research indicates that most emergency healthcare workers are not adequately prepared for disasters. Almukhlifi et al. noted that previous disaster experience improves preparedness [ 9 ]. Glow et al. showed that training tailored to local resources and involving all relevant disciplines yields better results than training conducted separately by each profession. When disciplines train in isolation, they lack shared understanding and effective team coordination; interdisciplinary training addresses this, fostering collaboration and clear roles during real incidents [ 10 ]. Recent research has examined the effectiveness of disaster training for specific professional groups, but significant gaps remain. Baetzner et al. studied disaster training programs for medical first responders (MFRs), focusing on measurable outcomes and using experimental evaluation methods like randomised controlled trials and pre–post testing. However, this focus excluded much of the existing literature that describes curriculum content, pedagogical strategies, and context-specific implementation, especially in settings where experimental evaluation isn't feasible. This narrow focus on formally validated outcomes missed innovative or locally relevant approaches that lack formal evaluation [ 11 ]. Similarly, Bahattab et al. performed a scoping review of humanitarian health education and training in low- and middle-income countries (LMICs). While offering valuable insights into international humanitarian responses, their review primarily focused on humanitarian aid frameworks and did not include training in high-income countries or clinical care outside humanitarian settings. Although they explored broader interdisciplinary humanitarian topics, they did not provide details on the structure and educational strategies of disaster and MCI training curricula tailored specifically for clinical healthcare learners and professionals [ 5 ]. These limitations underscore the importance of creating a synthesis that covers different income settings and specifically explores how disaster and MCI training curricula are structured, implemented, and assessed across various healthcare roles. This review surveys disaster and mass casualty incident (MCI) training programs used internationally in both prehospital and in-hospital settings, involving paramedics, nurses, interns, residents, and physicians. It highlights different curricular strategies, teaching methods, target groups, and areas lacking evidence to guide future curriculum design. Methods Study Design We conducted this scoping review as part of the MCIPHER (Mass-Casualty Incident - Prehospital Emergency Response) project, a collaborative effort focused on strengthening disaster medicine education, developing innovative training methods, and improving prehospital preparedness. We followed the methodological framework outlined by Arksey and O'Malley [ 12 ] and incorporated later recommendations by Levac and colleagues [ 13 ] to ensure thorough coverage and analytical rigor. Our reporting adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist [ 14 ]. We preemptively developed and registered our protocol on Protocols.io [ 15 ], with the full text published on preprints.org [ 16 ], to guarantee transparency and reproducibility. Stage 1: Identifying the Research Question We addressed the following primary and secondary research questions: Primary: To map how disaster preparedness and mass-casualty incident (MCI) education and training interventions for healthcare learners and professionals are designed, delivered, and evaluated worldwide, and to characterize the structural, pedagogical, and evaluative patterns that influence the current evidence base. Secondary: - What curriculum architectures characterize how interventions are structured and delivered? - What competency domains and content areas are addressed, and how do they align with operational response frameworks? - What instructional design, assessment, and operational frameworks underpin the training, and to what extent do these align with each other (curricular coherence)? - What outcome levels, mapped to Moore's Expanded Outcomes Framework, are reported across studies, and what evaluation patterns emerge? - What cognitive support tools and scaffolding approaches are used, and can these be categorized into a structured taxonomy? - What is the extent and nature of interprofessional training in disaster/MCI curricula, and how does it relate to team-based demands of real-world MCI response? Stage 2: Identifying Relevant Studies Search Strategy We searched seven academic databases on June 12, 2025: PubMed, Embase, Scopus, PsycINFO (via APA PsycNet), CINAHL, the Cochrane Library, and ClinicalTrials.gov. We used controlled vocabulary and free-text keywords related to "disaster medicine," "mass casualty incident," "training," "curriculum," and "simulation," with database-specific automatic term mapping to improve retrieval accuracy. An academic librarian contributed to developing and refining the strategy through iterative testing to enhance sensitivity and specificity. The Peer Review of Electronic Search Strategies (PRESS) guidelines were used to evaluate the initial search strategy [ 17 ]. We updated the search just before data extraction to include recently published studies. Example search strings for each database are provided in Appendix 1. Additionally, we manually reviewed the reference lists of included studies and relevant review articles to find further eligible publications. Stage 3: Study Selection Screening and Eligibility Criteria We imported all retrieved records into Covidence (Veritas Health Innovation, Melbourne, Australia), a web-based platform for managing systematic and scoping reviews [ 18 ]. Covidence automatically detects and removes duplicates, and handles title/abstract screening and full-text assessment. Inclusion Criteria: 1. Population: Healthcare providers (physicians, nurses, paramedics, EMTs, residents, interns) or university/college students enrolled in health-related fields (medicine, nursing, public health, EMS). 2. Concept: Describe or evaluate curricular design strategies in disaster medicine or MCI training, including curriculum structure, delivery methods, learning outcomes, or training modalities. 3. Context: Disaster medicine or mass casualty incident education and training, in prehospital or in-hospital settings. 4. Study Types: Primary studies (quantitative, qualitative, or mixed methods), curriculum development reports, and grey literature providing relevant curriculum detail. English language only; publications from the last 10 years. Exclusion Criteria: 1. Do not focus on disaster or MCI training (e.g., general emergency care, trauma surgery without a disaster context). 2. Involve non-healthcare populations (e.g., military, engineers, general public) without a separate analysis for healthcare participants. 3. Editorials, commentaries, or opinion pieces without empirical data or descriptive curriculum details. 4. Systematic reviews, literature reviews, or other scoping reviews. 5. Not available in full text. Stage 4: Data Charting Two reviewers independently pilot-tested a standardized data-charting template on five studies to verify clarity, reliability, and completeness. The template was revised as needed and remained adaptable, allowing iterative adjustments throughout the review. The extraction form collected data on study ID, DOI, and country of origin; educational setting and target population; outcome levels mapped to Moore's Expanded Outcomes Framework with corresponding descriptions and targeted competencies; instruments/tools, timing of training and evaluation activities, and referenced training design frameworks; assessment approaches and operational frameworks; and whether cognitive support strategies were provided. Two reviewers independently extracted data and then compared their findings to ensure consistency, resolving disagreements through discussion or, if necessary, adjudication by a third reviewer. We organized the extracted data in Microsoft Excel. Moore's Framework and Outcome Classification We utilized Moore's Expanded Outcomes Framework (Moore et al., 2009) to categorize outcomes in the included studies. This framework offers a more detailed differentiation than the Kirkpatrick model by distinguishing between declarative and procedural knowledge and separating simulated competence from actual performance, both of which are essential for interpreting evidence in disaster education. In line with the iterative approach of scoping reviews, Moore's framework was incorporated during the analysis phase to achieve greater precision than the initially registered Kirkpatrick model. A crosswalk between Kirkpatrick and Moore's models is available in Appendix 2 for comparisons with related reviews. Moore's seven levels (operational definitions): Level 1 (Participation): Attendance, enrollment, or completion of the training activity. Level 2 (Satisfaction): Learner satisfaction, perceived relevance, engagement, and confidence. Level 3a (Declarative Knowledge): Factual recall and recognition, usually through multiple-choice or true/false tests. Level 3b (Procedural Knowledge): Applied understanding, reasoning, and self-reported skill or attitude gains, typically via validated preparedness or competency scales. Level 4 (Competence): Demonstrated ability in controlled or simulated settings (triage scoring, simulation checklists, OSCE-style evaluations, global rating scales). Level 5 (Performance): Behavior change in real clinical practice or field deployment, assessed through supervisor evaluations or post-deployment feedback. Levels 6–7 (Patient and Community Health): Measurable patient-level and population, or system-level health outcomes. Studies that develop curricula through expert consensus or curriculum mapping without assessing learner outcomes were categorized as not classifiable. When an outcome could apply to multiple levels, classification was guided by the measure's primary purpose, the assessment environment (simulated vs. real-world), and pre-established decision rules developed through iterative team discussion. Each study could be assigned multiple Moore levels reflecting different outcome types measured within the same intervention. Stage 5: Collating, Summarizing, and Reporting Results We analyzed data using descriptive statistics (counts and frequencies) for quantitative results and narrative analysis for qualitative and contextual information. We organized the extracted data into key areas: general study characteristics; study design and populations; educational setting and targeted competencies; outcomes classified by Moore's framework; instructional, assessment, and operational frameworks; and data collection tools, assessment timing, and cognitive support provided. Development of Analytical Constructs Through an iterative process of comparison and clustering, we identified recurring patterns and derived three original analytical constructs: Curriculum Architecture Typology: We classified studies into four structural types based on curricular integration, duration, and progression logic: - Stand-Alone: Single delivery of up to 4 weeks, not integrated into an existing program. - Embedded: Component within an existing program. - Longitudinal: Spans multiple terms with sequential scaffolding. - Model Proposal: Expert consensus without learner outcome data. Curricular Coherence Analysis: We cross-tabulated each study's reported frameworks across three axes: (i) pedagogical or training design framework (e.g., ADDIE, curriculum mapping), (ii) assessment or evaluation framework (formal model or validated instrument), and (iii) operational response framework (ICS, NIMS, triage algorithm). "Alignment" was defined as explicit reference to or integration of all three axes. Cognitive Scaffolding Taxonomy: We inductively categorized cognitive support approaches into five categories based on their mechanism of action: Mnemonic Aids, Algorithmic Supports, Job Aids/Checklists, Scenario Scaffolding, and Mentoring/Team Support. This thorough mapping method allowed us to analyze how disaster and mass-casualty curricula are created, executed, and assessed across educational settings and learner groups, while also identifying structural patterns, evidence gaps, methodological issues, and priorities for future research and program development. Ethics Since this scoping review only integrated previously published, publicly accessible literature and did not involve direct interactions with human participants, formal ethics approval was not required. We adhered to established ethical standards, including transparent reporting of methodology, proper attribution and citation of sources, and avoidance of plagiarism or duplicate publication. Any potential conflicts of interest among the research team were disclosed and managed in accordance with institutional policies. Results Literature search and study selection Our search of the included databases and registers yielded 466 records, with five additional records identified through other sources. After removing 36 duplicates, we screened 435 records by title and abstract, excluding 326. Full-text assessment of 109 articles resulted in 68 exclusions. In total, 41 studies met the inclusion criteria. The selection process appears in the PRISMA flow diagram (Fig. 1). [Figure 1 here] Figure 1. PRISMA Flow Diagram Study characteristics The included studies covered geographically diverse regions, with the largest contribution from the United States [19–36]. Taiwan [37–39] and Turkey [40,41] provided notable studies. One study was conducted jointly across Belgium and Italy [42] (Table 1). Table 1. Overview of included studies Study ID Paper DOI Country Montana 2019 10.3352/jeehp.2019.16.19 France Levoy 2018 10.1017/dmp.2017.150 USA Chumvanichaya 2025 10.1186/s12245-025-00850-2 Thailand Mohammadinia 2022 10.34172/doh.2022.44 Iran Bajow 2015 10.5055/ajdm.2015.0197 Saudi Arabia Koziel 2015 10.3109/10903127.2014.967428 USA Shinchi 2019 10.1017/S1049023X19004564 Japan Wright 2025 10.1017/dmp.2025.74 USA Tsai 2020 10.1097/MD.0000000000020230 Taiwan Kim 2020 10.1016/j.nedt.2019.104297 South Korea Xia 2019 10.1111/phn.12685 China Kivlehan 2021 10.1017/S1049023X21000388 Haiti Eastwood 2023 10.1007/s43678-023-00601-3 Canada Hung 2021 10.3390/ijerph181910545 Hong Kong Hwang 2021 10.4040/jkan.21164 South Korea Hsieh 2023 10.1016/j.nedt.2023.105919 Taiwan Baser 2025 10.7717/peerj.18800 Turkey Cole 2021 10.3389/fpubh.2021.682112 USA Park 2024 10.1097/jnr.0000000000000596 South Korea Donahue 2022 10.1097/PEC.0000000000002318 USA Djalali 2017 10.1097/MEJ.0000000000000383 Italy Chilton 2017 10.1097/NNE.0000000000000341 USA Varanelli 2019 10.1016/j.teln.2019.06.005 USA Martin-Ibañez 2021 10.1016/j.nedt.2021.105051 Spain Evans 2018 10.1097/01.NEP.0000000000000308 USA Cicero 2017 doi.org/10.1080/10903127.2016.1235239 USA Chiang 2020 https://doi.org/10.1016/j.nedt.2020.104358 Taiwan Noh 2020 https://doi.org/10.1111/ijn.12810 South Korea Plaitano 2021 https://doi.org/10.1017/dmp.2021.318 USA Sarin 2017 10.1017/S1049023X17000267 USA Sandifer 2023 10.1017/S1049023X23000407 USA Ripoll-Gallardo 2020 10.1186/s13049-020-00778-x Italy Sandifer 2024 10.1017/S1049023X24000165 USA Alhawatmeh 2025 10.1016/j.ecns.2025.101749 Jordan Allen 2016 10.7205/MILMED-D-l 5-00084 USA Altman 2016 10.1017/dmp.2016.20 USA Anderson 2025 10.1016/j.profnurs.2025.03.005 USA Bahattab 2022 10.1017/S1049023X22001340 Italy & Belgium Genç 2025 10.1016/j.nedt.2025.106581 Turkey Sarin 2019 10.1017/S1049023X19004746 USA Schilly 2024 10.1017/dmp.2023.230 USA Publication output increased over time. The evidence base was limited before 2018, with only a small number of studies published between 2015 and 2017 [20,24,27,29,32,34,43,44]. A noticeable rise occurred in recent years, with the highest volume in 2021 and 2025 [21,22,28,33,40,41,45–50]. Year-by-year counts are shown in Fig. 2. [Figure 2 here] Figure 2. Yearly distribution of included studies by curriculum architecture type (2015–2025) Study designs varied among the 41 included studies (Table 2). Quasi-experimental approaches were most common [22,24,27,28,36,38,40,47,51–54], followed by pre–post or pretest–posttest evaluations [19,21,23,25,37,55,56]. Descriptive [26,30–32,34,45,57] and qualitative methods [20,33,43,44,48,58] mainly documented or proposed educational initiatives. Randomized controlled designs [41,49,50,59] and cross-sectional methods [29,39,42] were less frequently used. One study used a Modified Delphi approach [35]. Table 2. Study Characteristics Study Characteristics Study design n (%) Quasi-experimental 12 (29.3%) Descriptive study 7 (17.1%) Pre–post evaluation 7 (17.1%) Qualitative study 6 (14.6%) Randomized controlled trial 4 (9.8%) Cross-sectional design 3 (7.3%) Mixed methods 1 (2.4%) Modified Delphi study 1 (2.4%) Population n (%) Nursing students 11 (26.8%) Medical students 6 (14.6%) Interprofessional 6 (14.6%) Residents & Fellows 5(12.2%) Nurses 4 (9.8%) Physicians 3 (7.3%) EMS providers (mixed) 2 (4.9%) Paramedic students 2 (4.9%) EMTs 1 (2.4%) Pharmacy students 1 (2.4%) Educational setting n (%) Stand-alone training 14 (34.1%) Undergraduate degree-based (Bachelor’s) course component 13 (31.7%) Elective module within an undergraduate degree program 3 (7.3%) Education model proposal 3 (7.3%) Graduate medical education (Residency-based training) 3 (7.3%) Graduate medical education (Emergency Medicine residency and fellowship) 2 (4.9%) Graduate medical education (Fellowship-based training) 1 (2.4%) Hospital-based in-service training 1 (2.4%) Postgraduate degree program (EMDM) 1 (2.4%) Characteristics of included populations Study populations were diverse, including undergraduate students and practicing clinicians. Nursing students [19,24–26,41,46–48,52,55,59], medical students [36,37,40,43,51,56], and EMS-related learners [20,27,28,49,50] made up the largest single-profession groups. Interprofessional groups appeared in several studies [22,32–34,42,44], with only one explicitly focusing on an undergraduate interprofessional group [33]. While undergraduate participants dominated, postgraduate trainees appeared in several studies: emergency medicine residents and fellows [21,45], pediatric residents [23], and EM/EMS fellows through model proposals [30,31]. Two studies involved physicians in expert roles via program director contributions to model development [29,35] (Table 2). Training contexts and competency domains Disaster curricula covered a range of educational contexts (see Table 2). They outlined competencies across four key domains: (1) disaster systems and incident management, which includes ICS/NIMS principles, hospital readiness, and surge planning [19,26,30,31,35–37,53]; (2) clinical and triage skills for MCIs and CBRN/HazMat scenarios, such as decontamination and life-saving procedures [21,23,25,27,28,39,43,44,48,50,53,56,57,59]; (3) public health, community preparedness, and population response [24,37,41,42,54]; and (4) psychosocial, ethical-legal, leadership, and interprofessional teamwork skills [22,30–33,47]. Several studies detailed education models, including frameworks for geriatric disaster care, disaster nursing, and emergency medicine residency programs [34,35,58]. Appendix 3 provides detailed information on educational settings and competency mapping. Curriculum architecture typology The 41 studies included were classified into four curriculum architecture types (empirical studies n=34; model proposals n=7): Type A (Stand-Alone, n=14) consisted of discrete courses or training modules delivered independently. Type B (Embedded, n=16) involved curricula integrated into existing degree programs or professional development. Type C (Longitudinal, n=4) encompassed curricula spanning multiple years with sequential content development. Type D (Model Proposal, n=7) included studies proposing conceptual frameworks without formal implementation data. Cross-tabulation of curriculum architecture type with Moore outcome levels revealed patterns in outcome depth. Type C (Longitudinal) curricula showed proportionally greater representation at deeper Moore levels (L3b, L4, L5), with extended timeframes and sequential development seeming to facilitate deeper competence integration. Type A (Stand-Alone) curricula primarily measured L1–L2 outcomes, reflecting constraints of time-limited interventions (Fig. 3) (Table 3). [Figure 3 here] Figure 3. Curriculum architecture typology and typical Moore outcome depth Table 3. Cross-Tabulation of Curriculum Architecture Types with the Highest Moore Outcome Level Achieved Curriculum Architecture Type n L1 L2 L3a L3b L4 L5 NC Type A: Stand-Alone 14 1 7 5 4 3 1 3 Type B: Embedded 16 0 9 7 9 8 2 2 Type C: Longitudinal 4 0 3 2 3 3 2 0 Type D: Model Proposal 7 0 3 2 2 1 0 2 Total 41 1 22 16 18 15 5 7 Abbreviations: L1 = Participation; L2 = Satisfaction; L3a = Declarative Knowledge; L3b = Procedural Knowledge; L4 = Competence; L5 = Performance; NC = Not Classified. Cell values indicate the number of studies reporting outcomes at each level. Studies reporting multiple outcome levels were assigned to each applicable level; consequently, level totals exceed n = 41. Interprofessional training analysis Only 6 of 41 studies (14.6%) explicitly targeted interprofessional learner groups. Cross-tabulation revealed that interprofessional curricula achieved higher representation at advanced Moore levels (L4–L5) than uniprofessional curricula. However, the small number of interprofessional studies limits generalizability. Most studies used pre–post designs to measure short-term changes, with some including additional data points to track persistence over time [21,23,32,37,49,50,59] and follow-up through retention assessments or multi-source ratings [27,32,54]. Simulation-based curricula often combined written assessments with performance evaluations such as checklists, triage accuracy metrics, and global rating scales [27,28,32,39,47,49,50,53–55]. Qualitative methods involved debriefing interviews, focus groups, and open-ended reflections [20,33,43,48]. Expert-focused methods included modified Delphi processes, curriculum mapping, and surveys of program directors [29–31,35,44]. Data collection methods In 41 studies, data collection mostly used structured tools measuring knowledge, attitudes, confidence, preparedness, or satisfaction, using Likert scales and multiple-choice questions [19,22,23,36–38,40–42,46,47,51,52,57,59]. Training outcomes using Moore's expanded outcomes framework Out of 41 studies, outcome reporting aligned with Moore's framework as follows: Level 1 (Reaction), 1 study (2.4%); Level 2 (Learning—knowledge and skills), 22 studies (53.7%); Level 3a (Learning—declarative knowledge), 16 studies (39.0%); Level 3b (Learning—procedural knowledge), 18 studies (43.9%); Level 4 (Transfer—behavior change), 15 studies (36.6%); Level 5 (Results—patient/system outcomes), 5 studies (12.2%); Not classified, 7 studies (17.1%) (Fig. 4). [Figure 4 here] Figure 4. Distribution of Moore outcome levels across the 41 included studies A key asymmetry emerged: Of 41 studies, 16 (39.0%) measured L3a (declarative knowledge) outcomes, but only 8 of these (19.5% of all studies) also measured L3b (procedural knowledge). This gap highlights the common focus on factual knowledge without a similar emphasis on assessing knowledge application in practice settings. Five studies achieved Level 5 outcomes: Kivlehan et al. (2021), Plaitano et al. (2021), Ripoll-Gallardo et al. (2020), Allen et al. (2016), and Bahattab et al. (2022). These studies cover multiple architecture types (A, B, C) and professional groups. Four studies were under-classified: Koziel et al. (2015), Cole et al. (2021), Park et al. (2024), and Varanelli et al. (2019), with outcomes that might warrant a Level 4 or higher designation. No curriculum has achieved formal measurement of Moore Level 6/7 (organizational change outcomes), highlighting a significant evidence gap caused by methodological constraints and the scarcity of curricula specifically focused on system redesign (Table 4). Table 4. Distribution of Training Outcomes across Moore's Expanded Outcomes Framework Moore Outcome Level n % of Studies Representative Studies Level 1 — Participation/Reaction 1 2.4 — Level 2 — Satisfaction/Learning (self-report) 22 53.7 Multiple Level 3a — Declarative Knowledge 16 39.0 Multiple Level 3b — Procedural Knowledge 18 43.9 Multiple Level 4 — Competence (behavior change) 15 36.6 Multiple Level 5 — Performance (patient/system outcomes) 5 12.2 Kivlehan et al. 2021; Plaitano et al. 2021; Ripoll-Gallardo et al. 2020; Allen et al. 2016; Bahattab et al. 2022 Level 6/7 — Patient/Community Health 0 0.0 — Not Classified 7 17.1 — Note: Percentages sum to more than 100% as individual studies may report outcomes at multiple Moore levels. Studies classified at higher levels may also report lower-level outcomes. Frameworks guiding training design, evaluation, and operational practice Training design frameworks Instructional design frameworks and curriculum models varied among the included studies, although several did not report a guiding framework [20,29–31,35,50,57,59]. When described, approaches included competency-based and standards-driven designs such as outcome- and competency-based education models [42,43,54] and national or professional curricular standards [33,40]. Structured program frameworks like HSEEP and PennDemic [19,22] complemented experiential and adult-learner approaches, including andragogy [24,43], experiential learning, and clinical competence models [32,52], as well as cognitive or mastery-based methods [53,54]. Simulation-focused models, notably the Jeffries Simulation Model [47,52], and systematic design methods such as ADDIE and instructional systems design were also commonly used [27,44,53]. Study-specific approaches included quality improvement frameworks [21], flipped classroom delivery [51], game-based learning [38], backward curriculum design [49], disaster cycle structures [34,36], and disaster literacy models [41]. Assessment frameworks Assessment approaches varied widely across studies, with many not specifying a formal evaluation framework [21,23–25,27,28,34,36,37,39,40,42–45,51,55,56,58,59]. When mentioned, evaluation methods included institutional processes like EFEE course evaluations [57] and HSEEP "hot wash" [19], as well as theory-based models such as Keller's ARCS Motivation Model [49] and frameworks focused on preparedness or competency [29,46,47,52]. Formal models encompassed Transfer of Learning, AACN Essentials [26], and Kirkpatrick-based evaluations combined with modified Delphi techniques [53,54]. Qualitative research references included established analytic standards [20,33,48]. Other methods covered KAB-style outcome measures, RACE-aligned behavior scales [38], TIPS and MASCAL/STX grading schemes [32], disaster literacy assessments based on the Health Belief Model [41], and outcome prioritization through Delphi approaches [35]. Two studies used guideline-informed content mapping to evaluate curricula [30,31]. Operational frameworks underpinning tabletop exercises Incident management frameworks often serve as the foundation of curricula, typically referencing ICS and related all-hazards systems such as NIMS, HICS, and IMS [19,26,37,43,44,53,54,59]. Mass-casualty triage methods such as START and JumpSTART are commonly integrated [20,23,27,33,46,47,49,50,54–56,59], whereas variants such as SMART, SIEVE, SORT, and SALT are less frequently included [20,21,27,49]. The curricula also feature broader operational models, including the disaster management cycle and crisis standards of care [21,41,43,59], military trauma protocols like TCCC/DoD CPGs and ATLS [32,37,56], and specific procedures such as hazmat zoning [39], METHANE reporting [54], RACE fire response [38], and reverse triage techniques [26]. Cognitive support tools and scaffolding approaches Multiple studies have detailed cognitive support tools or scaffolding strategies integrated into simulations, drills, or practical learning environments. Algorithm-based cognitive support has been examined in a limited number of studies. Koziel et al. integrated formal triage algorithms (SMART, START/JumpSTART) into simulations, supported by physical aids such as triage kits, reference cards, and length-based tapes, along with structured debriefings [20]. Shinchi et al. employed the SINCHI mnemonic framework, which included structured information-gathering prompts and checklist-style tasks within simulation exercises [58]. Wright et al. placed large SALT triage reference cards in clinical areas to enable point-of-care access [21]. Kim et al. utilized Emergo Train System materials, such as patient cards with clinical details and treatment cues, to assist decision-making [55]. Many studies reported the use of facilitator-mediated cognitive support. This included real-time cueing, guiding questions, or immediate feedback during or right after scenarios to help learners make decisions and reflect [32,39,47,53,54,56]. One study highlighted mentorship-based scaffolding, where novice participants worked one-on-one with experienced EMTs for real-time guidance [28]. Other methods involved START-based triage support [46], the RACE procedure [38], and access to triage algorithms during simulations [27]. Genç et al. described disaster information cards, mobile apps, and serious games with embedded cues and feedback [41], while Altman et al. reported using structured prompts from educators [34]. Discussion What This Review Contributes This scoping review produces three main analytical outputs. We develop a taxonomy for disaster health curricula, categorizing 41 peer-reviewed studies based on architecture type (Stand-Alone, Embedded, Longitudinal, Model Proposal), professional discipline, and learner population. Additionally, we utilize Moore's expanded outcomes framework as a systematic analytical tool, highlighting significant gaps in outcome depth and showing that traditional Kirkpatrick classification masks important differences in competence assessment. Lastly, we identify two systemic challenges. The Declarative Knowledge Plateau and the Competence-Performance Gap that go beyond individual curricula and reflect broader limitations in how disaster health education is currently understood and evaluated. Moore Outcome Analysis: The Moore Ceiling Applying Moore's framework to this literature highlights a clear outcome ceiling. While 53.7% of studies measured Kirkpatrick Level 2 (Learning), corresponding to Moore L2, only 12.2% reached Moore L5 (patient or system outcomes), and none achieved L6 or L7 (organizational change). This pattern exemplifies what we call the Moore Ceiling: a systematic upper limit where disaster health education outcomes remain largely unexplored and unmeasured. Such measurement limits are widely acknowledged in broader medical education. Two key phenomena explain this ceiling. The Declarative Knowledge Plateau, where 16 studies assessed declarative knowledge (L3a) but only eight moved on to measure procedural knowledge (L3b), indicates curricula effectively teach factual disaster response information (e.g., triage systems, incident command structures), but less focus is placed on translating this into practical skills under real conditions. This may be due to assessment choices or curricular gaps. Secondly, the Competence-Performance Gap shows a 66% drop from L4 (behavioral transfer, 15 studies) to L5 (patient or system outcomes, five studies). Even curricula that change practitioner behavior may not demonstrate impact on patients or systems. This gap likely results from the influence of multiple factors on clinical outcomes. While individual competence is necessary, it alone does not suffice; system factors, team dynamics, resources, implementation fidelity, organizational readiness, and health system resources all affect outcomes. The Curricular Coherence Gap This review revealed a Curricular Coherence Gap: a lack of systematic alignment among learning objectives, curriculum content, instructional methods, and assessment. About 60% of studies did not clearly connect their competency goals to specific modules or assessments. This disconnect indicates that many curricula, despite covering relevant disaster health skills, lack a coherent logic model that links content delivery, teaching strategies, and evaluations to desired outcomes. Improving curricular coherence could be a key factor in enhancing the depth of learning outcomes (Fig. 5). [Figure 5 here] Figure 5. Curricular coherence: three-axis framework alignment across 41 studies Curriculum Architecture and Outcome Depth The cross-tabulation of curriculum architecture with Moore outcome levels indicates a link between architecture type and outcome depth. Longitudinal curricula (Type C), although only 9.8% of the sample, showed a higher proportion in L4–L5 outcomes (50% compared to the mean of 36.6%), based on four studies. This pattern suggests a need for future research, though the small sample size limits causal conclusions. Conversely, Stand-Alone curricula (Type A), which are often published and easy to implement immediately, mainly focus on L1–L2 outcomes. Embedded curricula (Type B) fell in between, with outcomes spread across L2–L4 and some L5. These findings imply that features such as duration, integration, and iterative refinement could enhance the potential of the outcome. Our curriculum architecture typology can also be related to Harden's Integration model, which describes a spectrum from discipline-specific to fully integrated curricula. Our typology aligns this concept with implementation timeframes and levels of institutional embedding. The Interprofessional Training Paradox The Interprofessional Training Paradox highlights a key structural challenge. Effective mass casualty response depends on smooth coordination among various professions, yet only 14.6% of disaster health curricula explicitly focus on interprofessional groups. This paradox might indicate publication bias, a real lack of such programs, or their existence mainly in gray literature. The six interprofessional studies showed a higher proportion of outcomes at levels L4–L5, suggesting that interprofessional design could lead to better transfer and better outcomes. Nonetheless, with only six studies, confidence in these outcome links is limited. We emphasize this as a research priority rather than a confirmed practice gap. Core Competencies and Cognitive Support Across curricula, triage (85.4%, 35 studies) and incident command systems (68.3%, 28 studies) were identified as nearly universal competency targets. These address the key challenges of mass casualty management: fairly distributing scarce resources and coordinating response teams spread out across different locations. However, few curricula explicitly assessed the integration of these skills; most evaluated them separately, which may hinder effective decision-making when clinical and logistical demands occur simultaneously. Cognitive support tools, such as mnemonics, decision trees, job aids, checklists, and scenario-based simulations, are a secondary focus across curricula, helping reduce cognitive load during complex, high-pressure decisions. The five-category taxonomy identified—comprising Mnemonic Aids, Algorithmic Supports, Job Aids and Checklists, Scenario Scaffolding, and Mentoring/Team Support—includes various methods that externalize decision logic to maintain performance under stress. This exploratory taxonomy, derived inductively, needs validation against established frameworks in cognitive load theory. Curricula that incorporate multiple scaffolding strategies within cohesive educational structures tend to produce better outcomes, indicating possible synergistic effects. Future Directions and Recommendations We offer five interconnected recommendations, each addressing a dimension of the identified evidence gaps. Curriculum Design: Future disaster health curricula should incorporate clear logic models that connect competency objectives with content, teaching methods, and assessment of outcomes. Whenever possible, long-term structures should be emphasized, as existing evidence indicates that sustained engagement improves the depth of outcomes. Interprofessional Integration: Curriculum developers should intentionally create programs that include mixed professional teams, mirroring real-world mass-casualty response scenarios. Additionally, funding and publication incentives should be aligned to encourage the development and evaluation of interprofessional curricula. Outcome Measurement: Evaluation strategies should focus on assessing outcomes at the highest level feasible within Moore's framework, and provide a clear justification if only levels L1–L3 are measured. For curricula deployed in the field, Moore L4 (transfer) should be the standard; for significant curriculum innovations, L5 (patient/system outcomes) should be given priority. This framework highlights outcome types often not fully captured by traditional assessments, such as skill decay, retention trajectories, team performance metrics, and organizational adoption of curricula. Curricula should systematically include a range of cognitive support strategies within a unified framework that covers both clinical and command decision-making. Research in implementation science should explore which combinations of scaffolding are most effective and identify the contextual factors that influence their success. Evidence Synthesis and Dissemination: The field needs ongoing systematic reviews that regularly update evidence regarding curriculum effectiveness, architecture types, and outcome trends. Discipline-specific societies should develop competency-based curriculum frameworks to steer future growth and ensure consistent measurement across programs. Limitations This review recognizes several key limitations. Firstly, assigning studies to Moore's outcome levels involved interpretive judgment about competence and behavior change, with the distinction between L3a (declarative competence) and L3b (procedural competence) requiring subjective interpretation. Future reviews could adopt more standardized outcome reporting standards to minimize ambiguity. Moreover, the assessment of curriculum coherence was qualitative, based solely on the authors' review of published descriptions, and lacked a systematic framework, limiting reproducibility. Second, many included studies did not clearly specify all curriculum components or the methods used to measure outcomes, likely due to limited publication space. Only two studies reported protocol deviations, suggesting that such deviations may be infrequent or underreported. There may also be publication bias favoring successful implementations, leading to an overestimation of the actual effectiveness. Third, the typology of curriculum architecture and the taxonomy of cognitive support tools were developed inductively from study characteristics and content analysis, respectively. Both serve as exploratory classifications whose validity, usefulness, and completeness for future comparative research still need to be confirmed. This review primarily examined peer-reviewed English-language publications since 2013, which means it may have overlooked important innovations documented in gray literature, non-English sources, or local reports. Following scoping review methodology, we did not perform a formal critical appraisal of the included studies, so we could not assess the evidence's strength and quality, nor draw definitive conclusions about effectiveness. The considerable variation across studies in populations, contexts, training methods, and outcome measures made direct comparisons difficult and precluded quantitative analysis. Most studies employed quasi-experimental, pre–post, or descriptive designs with limited follow-up, which constrained insights into long-term behavioral change and broader system impacts. Conclusion This scoping review of 41 peer-reviewed disaster health curricula reveals a maturing field with widespread adoption of evidence-based practices but persistent gaps in evidence at deeper levels of outcome measurement. Applying Moore's expanded outcomes framework uncovers the Moore Ceiling: a systematic limitation in how thoroughly curricula evaluate competence and behavior change. Three key challenges. The Declarative Knowledge Plateau, the Competence-Performance Gap, and the Curricular Coherence Gap highlight actionable areas for improvement. The low prevalence of interprofessional curricula (14.6% of the sample) indicates both a research gap and a practice necessity, given the inherently interprofessional nature of mass casualty response. Advancing deeper outcome measurement requires curriculum developers to implement clear logic models, prioritize longitudinal structures where feasible, and systematically include interprofessional design. The five studies demonstrating Moore L5 outcomes demonstrate that patient- and system-level impacts can be measured. Achieving this shift depends on aligning funding strategies with interprofessional curriculum development and establishing discipline-specific competency frameworks to unify outcome measurement across programs. Abbreviations MCI: Mass Casualty Incident; MCI-PHER: Mass-Casualty Incident — Prehospital Emergency Response; WHO: World Health Organization; EMS: Emergency Medical Services; PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews; PRESS: Peer Review of Electronic Search Strategies; ICS: Incident Command System; NIMS: National Incident Management System; HICS: Hospital Incident Command System; IPE: Interprofessional Education; CBRN: Chemical, Biological, Radiological, and Nuclear; START: Simple Triage and Rapid Treatment; SMART: Sieve, Military, Assess, Respiratory, Treatment; SALT: Sort, Assess, Lifesaving Interventions, Treatment/Transport; OSCE: Objective Structured Clinical Examination; EMAP: Emergency Management Accreditation Program; ICRC: International Committee of the Red Cross; MSF: Médecins Sans Frontières; LMICs: Low- and Middle-Income Countries; ADDIE: Analysis, Design, Development, Implementation, Evaluation; HSEEP: Homeland Security Exercise and Evaluation Program; KAB: Knowledge, Attitudes, and Behavior; EMDM: European Master in Disaster Medicine; EMT: Emergency Medical Technician Declarations Acknowledgements The authors thank Dr Sarah Kazim, Chair of Emergency Medicine at Dubai Health, for her support and for fostering the departmental environment that helped make this work possible. They also thank Mohammed Bin Rashid University of Medicine and Health Sciences for its support and collaboration in this research, Shakeel Tegginmani of the Al Maktoum Medical Library, for assistance with the literature search, and the Institute of Learning (IOL), for research support. Authors' contributions N.M.A: Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing. A.O: Conceptualization, Data curation, Methodology, Formal analysis, Supervision, Writing – review & editing. S.A.E: Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing. M.A: Methodology, Writing – review & editing. H.H.Y: Investigation, Resources, Data curation, Writing – review & editing. A.Y: Conceptualization, Project administration, Resources, Supervision, Writing – review & editing. I.H: Supervision, Methodology, Writing – review & editing. N.Z: Conceptualization, Visualization, Supervision, Methodology, Writing – review & editing. Funding This research was funded by the Mohammed Bin Rashid University of Medicine and Health Sciences through the Dubai Health Collaborative Stimulus Research Grant (CSRG) for the M-CIPHER project. The funding organization had no influence on the study design, data analysis, or manuscript preparation. Data availability All data generated or analysed during this study are included in this published article and its additional files. The review protocol is available on Protocols.io. Clinical Trial Number Not applicable Ethics approval and consent to participate Not applicable. This scoping review synthesized only previously published, publicly available literature and involved no direct contact with human participants. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References World Health Organization. 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Supplementary Files Appendix1.docx Appendix2.docx Appendix3.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 16 Apr, 2026 Editor invited by journal 31 Mar, 2026 Editor assigned by journal 29 Mar, 2026 Submission checks completed at journal 29 Mar, 2026 First submitted to journal 26 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9230758","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625808431,"identity":"290c00cd-cb14-4964-a192-ac6cde71fcc4","order_by":0,"name":"Naglaa Mohamed Abdelhamied","email":"","orcid":"","institution":"Emergency Department, Dubai Health","correspondingAuthor":false,"prefix":"","firstName":"Naglaa","middleName":"Mohamed","lastName":"Abdelhamied","suffix":""},{"id":625808432,"identity":"344ae02c-a362-4b60-8695-eaf3e82742b7","order_by":1,"name":"Abu Omayer","email":"","orcid":"","institution":"Institute of Learning, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Health","correspondingAuthor":false,"prefix":"","firstName":"Abu","middleName":"","lastName":"Omayer","suffix":""},{"id":625808437,"identity":"6b7837e2-2cbd-40e5-8599-8c65bb209cab","order_by":2,"name":"Salma Abdalla Elmisbah","email":"","orcid":"","institution":"Emergency Department, Dubai Health","correspondingAuthor":false,"prefix":"","firstName":"Salma","middleName":"Abdalla","lastName":"Elmisbah","suffix":""},{"id":625808440,"identity":"7bd2d3a9-ca7d-439f-8ac0-792075ee9f2f","order_by":3,"name":"Mohamed Alali","email":"","orcid":"","institution":"Institute of Learning, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Health","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Alali","suffix":""},{"id":625808443,"identity":"eb3652f8-e865-4172-bfc9-bcf4822ea25d","order_by":4,"name":"Hossam Hassan Yussef","email":"","orcid":"","institution":"Emergency Department, Dubai Health","correspondingAuthor":false,"prefix":"","firstName":"Hossam","middleName":"Hassan","lastName":"Yussef","suffix":""},{"id":625808450,"identity":"2abf775c-89f2-4d5e-87e4-cf471a6d7293","order_by":5,"name":"Azza Yousif","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIie3SMUvEMBTA8SeBnEOPrK8ova/wQiA6iJ+lx4FTB8HB5Th7HNwth7MO4lfwFudCoF36ARwcKkKnDk7iENT2wO1ydHTIfwoJP5IHAfD5/m8oBMQcgDKAAfBeJLxP/whjvQiET1lHoAcRq7V8D+wpqiLR1dflayQWIq9geuZ+UVkqNVwi6rI5kWuqFRo2IMgv3Ne8JPpomOJMtwsMyIxTwzgCN04x6khgEdVdokNL5uZxS75/nIS2hCMSdgsyMXXkYJk5iSzzK/nQzoJlfa2OyciNYRrHtxMniYrFpmrsDMVq8vzWWDOKinmNH5/n7vHhkHZsxntA+z+qvcc+n8/ng18FWVBBn7N0IgAAAABJRU5ErkJggg==","orcid":"","institution":"Emergency Department, Dubai Health","correspondingAuthor":true,"prefix":"","firstName":"Azza","middleName":"","lastName":"Yousif","suffix":""},{"id":625808453,"identity":"934b43c5-9351-48fb-a707-d2d9db6e8fc2","order_by":6,"name":"Ives Hubloue","email":"","orcid":"","institution":"Emergency Department, UZ Brussel University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ives","middleName":"","lastName":"Hubloue","suffix":""},{"id":625808458,"identity":"fdb406d5-7ebb-4333-b0cd-b61eff25c003","order_by":7,"name":"Nabil Zary","email":"","orcid":"","institution":"Institute of Learning, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Health","correspondingAuthor":false,"prefix":"","firstName":"Nabil","middleName":"","lastName":"Zary","suffix":""}],"badges":[],"createdAt":"2026-03-26 07:38:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9230758/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9230758/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107870409,"identity":"66c41393-e3b5-4898-8f91-ae64aa9bbb7e","added_by":"auto","created_at":"2026-04-27 07:39:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":225297,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flow diagram illustrating the study selection process. Database searches identified 466 records, and 5 additional records were identified from other sources. After removing 36 duplicates, 435 records were screened based on title and abstract (326 were excluded). Full-text assessment of 109 articles led to 68 exclusions, yielding 41 studies for inclusion.\u003c/p\u003e","description":"","filename":"Figure1PRISMAFlowDiagram.png","url":"https://assets-eu.researchsquare.com/files/rs-9230758/v1/ab30dda0f92582eeb97f6230.png"},{"id":107870286,"identity":"55ca711c-f874-46cd-b093-9475cf602355","added_by":"auto","created_at":"2026-04-27 07:39:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":252454,"visible":true,"origin":"","legend":"\u003cp\u003eYearly distribution of included studies by curriculum architecture type (2015–2025). Stacked bar chart showing the number of studies published per year, stratified by architecture type (Type A: Stand-Alone, Type B: Embedded, Type C: Longitudinal, Type D: Model Proposal), with a cumulative total overlay. Bars are coloured by architecture type to illustrate the evolving composition of the disaster health curriculum literature.\u003c/p\u003e","description":"","filename":"Figure2YearlyDistributionArchitecture.png","url":"https://assets-eu.researchsquare.com/files/rs-9230758/v1/2cb6597411ef07a96fa96ca6.png"},{"id":107870432,"identity":"7f2de4ab-5494-4dc1-bba3-86faabe921e8","added_by":"auto","created_at":"2026-04-27 07:39:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153328,"visible":true,"origin":"","legend":"\u003cp\u003eCurriculum architecture typology and typical Moore outcome depth. Range plot displaying the four inductively derived curriculum architecture types (A: Stand-Alone, n=14; B: Embedded, n=16; C: Longitudinal, n=4; D: Model Proposal, n=7) along the x-axis, with Moore outcome levels (L1–L7) on the y-axis. Shaded ranges indicate the span of outcome levels typically achieved by each architecture type; ranges may overlap across types.\u003c/p\u003e","description":"","filename":"Figure3CurriculumArchitectureTypology.png","url":"https://assets-eu.researchsquare.com/files/rs-9230758/v1/ddd73eab460cf026d3c29680.png"},{"id":107870278,"identity":"5c255dd1-ad32-480a-b07e-4b3d68078a9f","added_by":"auto","created_at":"2026-04-27 07:39:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":277752,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Moore outcome levels across the 41 included studies. Horizontal bar chart showing the number and percentage of studies reporting outcomes at each level of Moore's Expanded Outcomes Framework (y-axis: Level 1 Participation through Level 7 Community Health, plus Not Classified; x-axis: number of studies). Studies may report outcomes at multiple levels; totals therefore exceed n=41. Colour coding distinguishes individual Moore levels.\u003c/p\u003e","description":"","filename":"Figure4MooreDistribution.png","url":"https://assets-eu.researchsquare.com/files/rs-9230758/v1/05eaf73f32d0b210f13a5141.png"},{"id":107832380,"identity":"acf21c56-5597-49a3-a7d1-d878e5ff471d","added_by":"auto","created_at":"2026-04-26 15:32:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":146550,"visible":true,"origin":"","legend":"\u003cp\u003eCurricular coherence: three-axis framework alignment across 41 studies. Bar chart categorising studies by the number of explicit framework axes present (0, 1, 2, or 3 of: training design framework, assessment/evaluation framework, operational response framework). Studies demonstrating alignment across all three axes are classified as having high curricular coherence.\u003c/p\u003e","description":"","filename":"Figure5CurricularCoherence.png","url":"https://assets-eu.researchsquare.com/files/rs-9230758/v1/09be0dc618e790837ba55a10.png"},{"id":108006790,"identity":"b18fe01e-1fd5-44d2-b106-80b4b823f733","added_by":"auto","created_at":"2026-04-28 12:57:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1341886,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9230758/v1/ac95405d-7390-4373-9cd0-692ed8f728fb.pdf"},{"id":107832374,"identity":"bce2d893-e074-4680-8425-af32bdb2c97f","added_by":"auto","created_at":"2026-04-26 15:32:29","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":36215,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9230758/v1/78eb1275b7dc0e96bb29dacd.docx"},{"id":107832376,"identity":"9f81db19-6959-44ac-b890-98a41156017c","added_by":"auto","created_at":"2026-04-26 15:32:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":34804,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9230758/v1/91c9c4dd22cd1ac24d871170.docx"},{"id":107832378,"identity":"acbd2d0e-7b7f-4eef-a912-ff7cf6c1dde7","added_by":"auto","created_at":"2026-04-26 15:32:29","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":50946,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix3.docx","url":"https://assets-eu.researchsquare.com/files/rs-9230758/v1/8b4959af35231fc24388916a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Curriculum Design in Disaster Medicine and Mass Casualty Incident Training: A Scoping Review of Healthcare Education","fulltext":[{"header":"Background","content":"\u003cp\u003eWhen a sudden-impact disaster strikes, emergency medical services are often overwhelmed as the number and severity of casualties surpass available resources. This causes systemic strain and creates an urgent need for coordinated action. Disasters, whether natural or human-made, such as terrorism, transportation accidents, fires, or extreme weather, can lead to these mass casualty incidents (MCIs). While many multi-casualty events involve large numbers of patients, they are usually manageable with current resources; however, if conditions worsen and resources are depleted, these incidents can escalate into full-scale MCIs, overwhelming response systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEffective MCI response relies on coordinated actions starting with scene recognition and continuing through quick triage and resource deployment to minimize preventable deaths and disabilities. To enhance preparedness, the World Health Organization (WHO) has provided guidance for healthcare providers and institutions dealing with MCI situations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMCIs encompass various categories that mirror the range of threats. Planned mass gatherings, such as major sporting events, as well as typical periodic incidents like transportation accidents, fires, and severe weather, each require specific planning. Additionally, incidents involving hazardous exposures to chemical, biological, or radiological agents form another category. Nuclear-related events, whether accidental, intentional, or acts of terrorism, are also significant. The most critical are catastrophic public health emergencies, including nuclear detonations, large-scale explosions, major hurricanes, and widespread outbreaks, which pose the greatest challenges for MCI response efforts [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResponse begins when the first emergency personnel at the scene identify an MCI. Handling these incidents involves a structured approach that typically consists of five key components: ensuring scene safety, conducting a scene assessment, transmitting information to incident command, preparing the site for casualties, and applying the START (Simple Triage and Rapid Treatment) protocol. This framework helps create order amid chaos and serves as the foundation for disaster response training [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDisasters, armed conflicts, and pandemics are happening more frequently, increasing the importance of MCI (Mass Casualty Incident) preparedness across healthcare systems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The success of humanitarian and emergency responses depends heavily on strong collaboration within and across different professional and disciplinary groups. Over the years, global crises have influenced how organizations train and prepare their personnel [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The International Committee of the Red Cross (ICRC) began offering humanitarian health courses in the 1970s, followed soon after by M\u0026eacute;decins Sans Fronti\u0026egrave;res (MSF). As the scale and complexity of disasters grew, universities began including humanitarian health education in the 1990s. The September 11 attacks shifted healthcare preparedness to a national security priority, leading to increased federal funding in the USA and a stronger focus on public health infrastructure and hospital readiness for large-scale emergencies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmergency preparedness involves four key phases. According to the Emergency Management Accreditation Program (EMAP), these are mitigation (reducing hazard risks and severity), preparedness (planning to enhance capabilities and capacity), response (ensuring safe and efficient operations during a disaster), and recovery (supporting restoration and rehabilitation afterward) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite these frameworks, research indicates that most emergency healthcare workers are not adequately prepared for disasters. Almukhlifi et al. noted that previous disaster experience improves preparedness [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Glow et al. showed that training tailored to local resources and involving all relevant disciplines yields better results than training conducted separately by each profession. When disciplines train in isolation, they lack shared understanding and effective team coordination; interdisciplinary training addresses this, fostering collaboration and clear roles during real incidents [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent research has examined the effectiveness of disaster training for specific professional groups, but significant gaps remain. Baetzner et al. studied disaster training programs for medical first responders (MFRs), focusing on measurable outcomes and using experimental evaluation methods like randomised controlled trials and pre\u0026ndash;post testing. However, this focus excluded much of the existing literature that describes curriculum content, pedagogical strategies, and context-specific implementation, especially in settings where experimental evaluation isn't feasible. This narrow focus on formally validated outcomes missed innovative or locally relevant approaches that lack formal evaluation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, Bahattab et al. performed a scoping review of humanitarian health education and training in low- and middle-income countries (LMICs). While offering valuable insights into international humanitarian responses, their review primarily focused on humanitarian aid frameworks and did not include training in high-income countries or clinical care outside humanitarian settings. Although they explored broader interdisciplinary humanitarian topics, they did not provide details on the structure and educational strategies of disaster and MCI training curricula tailored specifically for clinical healthcare learners and professionals [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese limitations underscore the importance of creating a synthesis that covers different income settings and specifically explores how disaster and MCI training curricula are structured, implemented, and assessed across various healthcare roles.\u003c/p\u003e \u003cp\u003eThis review surveys disaster and mass casualty incident (MCI) training programs used internationally in both prehospital and in-hospital settings, involving paramedics, nurses, interns, residents, and physicians. It highlights different curricular strategies, teaching methods, target groups, and areas lacking evidence to guide future curriculum design.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eWe conducted this scoping review as part of the MCIPHER (Mass-Casualty Incident - Prehospital Emergency Response) project, a collaborative effort focused on strengthening disaster medicine education, developing innovative training methods, and improving prehospital preparedness. We followed the methodological framework outlined by Arksey and O'Malley [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and incorporated later recommendations by Levac and colleagues [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] to ensure thorough coverage and analytical rigor. Our reporting adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We preemptively developed and registered our protocol on Protocols.io [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], with the full text published on preprints.org [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], to guarantee transparency and reproducibility.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStage 1: Identifying the Research Question\u003c/h3\u003e\n\u003cp\u003eWe addressed the following primary and secondary research questions:\u003c/p\u003e \u003cp\u003ePrimary: To map how disaster preparedness and mass-casualty incident (MCI) education and training interventions for healthcare learners and professionals are designed, delivered, and evaluated worldwide, and to characterize the structural, pedagogical, and evaluative patterns that influence the current evidence base.\u003c/p\u003e \u003cp\u003eSecondary: - What curriculum architectures characterize how interventions are structured and delivered? - What competency domains and content areas are addressed, and how do they align with operational response frameworks? - What instructional design, assessment, and operational frameworks underpin the training, and to what extent do these align with each other (curricular coherence)? - What outcome levels, mapped to Moore's Expanded Outcomes Framework, are reported across studies, and what evaluation patterns emerge? - What cognitive support tools and scaffolding approaches are used, and can these be categorized into a structured taxonomy? - What is the extent and nature of interprofessional training in disaster/MCI curricula, and how does it relate to team-based demands of real-world MCI response?\u003c/p\u003e\n\u003ch3\u003eStage 2: Identifying Relevant Studies\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSearch Strategy\u003c/h2\u003e \u003cp\u003eWe searched seven academic databases on June 12, 2025: PubMed, Embase, Scopus, PsycINFO (via APA PsycNet), CINAHL, the Cochrane Library, and ClinicalTrials.gov. We used controlled vocabulary and free-text keywords related to \"disaster medicine,\" \"mass casualty incident,\" \"training,\" \"curriculum,\" and \"simulation,\" with database-specific automatic term mapping to improve retrieval accuracy.\u003c/p\u003e \u003cp\u003eAn academic librarian contributed to developing and refining the strategy through iterative testing to enhance sensitivity and specificity. The Peer Review of Electronic Search Strategies (PRESS) guidelines were used to evaluate the initial search strategy [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. We updated the search just before data extraction to include recently published studies. Example search strings for each database are provided in Appendix 1. Additionally, we manually reviewed the reference lists of included studies and relevant review articles to find further eligible publications.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStage 3: Study Selection\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eScreening and Eligibility Criteria\u003c/h2\u003e \u003cp\u003eWe imported all retrieved records into Covidence (Veritas Health Innovation, Melbourne, Australia), a web-based platform for managing systematic and scoping reviews [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Covidence automatically detects and removes duplicates, and handles title/abstract screening and full-text assessment.\u003c/p\u003e \u003cp\u003eInclusion Criteria: 1. Population: Healthcare providers (physicians, nurses, paramedics, EMTs, residents, interns) or university/college students enrolled in health-related fields (medicine, nursing, public health, EMS). 2. Concept: Describe or evaluate curricular design strategies in disaster medicine or MCI training, including curriculum structure, delivery methods, learning outcomes, or training modalities. 3. Context: Disaster medicine or mass casualty incident education and training, in prehospital or in-hospital settings. 4. Study Types: Primary studies (quantitative, qualitative, or mixed methods), curriculum development reports, and grey literature providing relevant curriculum detail. English language only; publications from the last 10 years.\u003c/p\u003e \u003cp\u003eExclusion Criteria: 1. Do not focus on disaster or MCI training (e.g., general emergency care, trauma surgery without a disaster context). 2. Involve non-healthcare populations (e.g., military, engineers, general public) without a separate analysis for healthcare participants. 3. Editorials, commentaries, or opinion pieces without empirical data or descriptive curriculum details. 4. Systematic reviews, literature reviews, or other scoping reviews. 5. Not available in full text.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStage 4: Data Charting\u003c/h3\u003e\n\u003cp\u003eTwo reviewers independently pilot-tested a standardized data-charting template on five studies to verify clarity, reliability, and completeness. The template was revised as needed and remained adaptable, allowing iterative adjustments throughout the review.\u003c/p\u003e \u003cp\u003eThe extraction form collected data on study ID, DOI, and country of origin; educational setting and target population; outcome levels mapped to Moore's Expanded Outcomes Framework with corresponding descriptions and targeted competencies; instruments/tools, timing of training and evaluation activities, and referenced training design frameworks; assessment approaches and operational frameworks; and whether cognitive support strategies were provided. Two reviewers independently extracted data and then compared their findings to ensure consistency, resolving disagreements through discussion or, if necessary, adjudication by a third reviewer. We organized the extracted data in Microsoft Excel.\u003c/p\u003e\n\u003ch3\u003eMoore's Framework and Outcome Classification\u003c/h3\u003e\n\u003cp\u003eWe utilized Moore's Expanded Outcomes Framework (Moore et al., 2009) to categorize outcomes in the included studies. This framework offers a more detailed differentiation than the Kirkpatrick model by distinguishing between declarative and procedural knowledge and separating simulated competence from actual performance, both of which are essential for interpreting evidence in disaster education. In line with the iterative approach of scoping reviews, Moore's framework was incorporated during the analysis phase to achieve greater precision than the initially registered Kirkpatrick model. A crosswalk between Kirkpatrick and Moore's models is available in Appendix 2 for comparisons with related reviews.\u003c/p\u003e \u003cp\u003eMoore's seven levels (operational definitions): Level 1 (Participation): Attendance, enrollment, or completion of the training activity. Level 2 (Satisfaction): Learner satisfaction, perceived relevance, engagement, and confidence. Level 3a (Declarative Knowledge): Factual recall and recognition, usually through multiple-choice or true/false tests. Level 3b (Procedural Knowledge): Applied understanding, reasoning, and self-reported skill or attitude gains, typically via validated preparedness or competency scales. Level 4 (Competence): Demonstrated ability in controlled or simulated settings (triage scoring, simulation checklists, OSCE-style evaluations, global rating scales). Level 5 (Performance): Behavior change in real clinical practice or field deployment, assessed through supervisor evaluations or post-deployment feedback. Levels 6\u0026ndash;7 (Patient and Community Health): Measurable patient-level and population, or system-level health outcomes.\u003c/p\u003e \u003cp\u003eStudies that develop curricula through expert consensus or curriculum mapping without assessing learner outcomes were categorized as not classifiable. When an outcome could apply to multiple levels, classification was guided by the measure's primary purpose, the assessment environment (simulated vs. real-world), and pre-established decision rules developed through iterative team discussion. Each study could be assigned multiple Moore levels reflecting different outcome types measured within the same intervention.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStage 5: Collating, Summarizing, and Reporting Results\u003c/h2\u003e \u003cp\u003eWe analyzed data using descriptive statistics (counts and frequencies) for quantitative results and narrative analysis for qualitative and contextual information. We organized the extracted data into key areas: general study characteristics; study design and populations; educational setting and targeted competencies; outcomes classified by Moore's framework; instructional, assessment, and operational frameworks; and data collection tools, assessment timing, and cognitive support provided.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of Analytical Constructs\u003c/h2\u003e \u003cp\u003eThrough an iterative process of comparison and clustering, we identified recurring patterns and derived three original analytical constructs:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCurriculum Architecture Typology: We classified studies into four structural types based on curricular integration, duration, and progression logic: - Stand-Alone: Single delivery of up to 4 weeks, not integrated into an existing program. - Embedded: Component within an existing program. - Longitudinal: Spans multiple terms with sequential scaffolding. - Model Proposal: Expert consensus without learner outcome data.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCurricular Coherence Analysis: We cross-tabulated each study's reported frameworks across three axes: (i) pedagogical or training design framework (e.g., ADDIE, curriculum mapping), (ii) assessment or evaluation framework (formal model or validated instrument), and (iii) operational response framework (ICS, NIMS, triage algorithm). \"Alignment\" was defined as explicit reference to or integration of all three axes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCognitive Scaffolding Taxonomy: We inductively categorized cognitive support approaches into five categories based on their mechanism of action: Mnemonic Aids, Algorithmic Supports, Job Aids/Checklists, Scenario Scaffolding, and Mentoring/Team Support.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis thorough mapping method allowed us to analyze how disaster and mass-casualty curricula are created, executed, and assessed across educational settings and learner groups, while also identifying structural patterns, evidence gaps, methodological issues, and priorities for future research and program development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003eSince this scoping review only integrated previously published, publicly accessible literature and did not involve direct interactions with human participants, formal ethics approval was not required. We adhered to established ethical standards, including transparent reporting of methodology, proper attribution and citation of sources, and avoidance of plagiarism or duplicate publication. Any potential conflicts of interest among the research team were disclosed and managed in accordance with institutional policies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eLiterature search and study selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur search of the included databases and registers yielded 466 records, with five additional records identified through other sources. After removing 36 duplicates, we screened 435 records by title and abstract, excluding 326. Full-text assessment of 109 articles resulted in 68 exclusions. In total, 41 studies met the inclusion criteria. The selection process appears in the PRISMA flow diagram (Fig. 1).\u003c/p\u003e\n\u003cp\u003e[Figure 1 here]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1. PRISMA Flow Diagram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe included studies covered geographically diverse regions, with the largest contribution from the United States [19–36]. Taiwan [37–39] and Turkey [40,41] provided notable studies. One study was conducted jointly across Belgium and Italy [42] (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Overview of included studies\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePaper DOI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMontana 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.3352/jeehp.2019.16.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevoy 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1017/dmp.2017.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChumvanichaya 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;10.1186/s12245-025-00850-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThailand\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMohammadinia 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.34172/doh.2022.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBajow 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.5055/ajdm.2015.0197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSaudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKoziel 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.3109/10903127.2014.967428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShinchi 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1017/S1049023X19004564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWright 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1017/dmp.2025.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTsai 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1097/MD.0000000000020230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTaiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKim 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1016/j.nedt.2019.104297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eXia 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1111/phn.12685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKivlehan 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1017/S1049023X21000388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHaiti\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEastwood 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1007/s43678-023-00601-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHung 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.3390/ijerph181910545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHong Kong\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHwang 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;10.4040/jkan.21164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHsieh 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1016/j.nedt.2023.105919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTaiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBaser 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.7717/peerj.18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCole 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;10.3389/fpubh.2021.682112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePark 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1097/jnr.0000000000000596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDonahue 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1097/PEC.0000000000002318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDjalali 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1097/MEJ.0000000000000383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eItaly\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChilton 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1097/NNE.0000000000000341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVaranelli 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1016/j.teln.2019.06.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMartin-Ibañez 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;10.1016/j.nedt.2021.105051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEvans 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1097/01.NEP.0000000000000308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCicero 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edoi.org/10.1080/10903127.2016.1235239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChiang 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://doi.org/10.1016/j.nedt.2020.104358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTaiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNoh 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://doi.org/10.1111/ijn.12810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlaitano 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://doi.org/10.1017/dmp.2021.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSarin 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1017/S1049023X17000267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSandifer 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1017/S1049023X23000407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRipoll-Gallardo 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1186/s13049-020-00778-x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eItaly\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSandifer 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1017/S1049023X24000165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlhawatmeh 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1016/j.ecns.2025.101749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJordan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAllen 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.7205/MILMED-D-l 5-00084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAltman 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1017/dmp.2016.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnderson 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1016/j.profnurs.2025.03.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBahattab 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1017/S1049023X22001340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eItaly \u0026amp; Belgium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGenç 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1016/j.nedt.2025.106581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSarin 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1017/S1049023X19004746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSchilly 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1017/dmp.2023.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePublication output increased over time. The evidence base was limited before 2018, with only a small number of studies published between 2015 and 2017 [20,24,27,29,32,34,43,44]. A noticeable rise occurred in recent years, with the highest volume in 2021 and 2025 [21,22,28,33,40,41,45–50]. Year-by-year counts are shown in Fig. 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Figure 2 here]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2. Yearly distribution of included studies by curriculum architecture type (2015–2025)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy designs varied among the 41 included studies (Table 2). Quasi-experimental approaches were most common [22,24,27,28,36,38,40,47,51–54], followed by pre–post or pretest–posttest evaluations [19,21,23,25,37,55,56]. Descriptive [26,30–32,34,45,57] and qualitative methods [20,33,43,44,48,58] mainly documented or proposed educational initiatives. Randomized controlled designs [41,49,50,59] and cross-sectional methods [29,39,42] were less frequently used. One study used a Modified Delphi approach [35].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Study Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQuasi-experimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDescriptive study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre–post evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQualitative study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRandomized controlled trial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCross-sectional design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMixed methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModified Delphi study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNursing students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (26.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedical students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterprofessional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResidents \u0026amp; Fellows\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5(12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhysicians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEMS providers (mixed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParamedic students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEMTs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePharmacy students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational setting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStand-alone training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (34.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUndergraduate degree-based (Bachelor’s) course component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eElective module within an undergraduate degree program\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEducation model proposal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGraduate medical education (Residency-based training)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGraduate medical education (Emergency Medicine residency and fellowship)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGraduate medical education (Fellowship-based training)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHospital-based in-service training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostgraduate degree program (EMDM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of included populations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy populations were diverse, including undergraduate students and practicing clinicians. Nursing students [19,24–26,41,46–48,52,55,59], medical students [36,37,40,43,51,56], and EMS-related learners [20,27,28,49,50] made up the largest single-profession groups. Interprofessional groups appeared in several studies [22,32–34,42,44], with only one explicitly focusing on an undergraduate interprofessional group [33].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile undergraduate participants dominated, postgraduate trainees appeared in several studies: emergency medicine residents and fellows [21,45], pediatric residents [23], and EM/EMS fellows through model proposals [30,31]. Two studies involved physicians in expert roles via program director contributions to model development [29,35] (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTraining contexts and competency domains\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDisaster curricula covered a range of educational contexts (see Table 2). They outlined competencies across four key domains: (1) disaster systems and incident management, which includes ICS/NIMS principles, hospital readiness, and surge planning [19,26,30,31,35–37,53]; (2) clinical and triage skills for MCIs and CBRN/HazMat scenarios, such as decontamination and life-saving procedures [21,23,25,27,28,39,43,44,48,50,53,56,57,59]; (3) public health, community preparedness, and population response [24,37,41,42,54]; and (4) psychosocial, ethical-legal, leadership, and interprofessional teamwork skills [22,30–33,47]. Several studies detailed education models, including frameworks for geriatric disaster care, disaster nursing, and emergency medicine residency programs [34,35,58]. Appendix 3 provides detailed information on educational settings and competency mapping.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCurriculum architecture typology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 41 studies included were classified into four curriculum architecture types (empirical studies n=34; model proposals n=7): Type A (Stand-Alone, n=14) consisted of discrete courses or training modules delivered independently. Type B (Embedded, n=16) involved curricula integrated into existing degree programs or professional development. Type C (Longitudinal, n=4) encompassed curricula spanning multiple years with sequential content development. Type D (Model Proposal, n=7) included studies proposing conceptual frameworks without formal implementation data.\u003c/p\u003e\n\u003cp\u003eCross-tabulation of curriculum architecture type with Moore outcome levels revealed patterns in outcome depth. Type C (Longitudinal) curricula showed proportionally greater representation at deeper Moore levels (L3b, L4, L5), with extended timeframes and sequential development seeming to facilitate deeper competence integration. Type A (Stand-Alone) curricula primarily measured L1–L2 outcomes, reflecting constraints of time-limited interventions (Fig. 3) (Table 3).\u003c/p\u003e\n\u003cp\u003e[Figure 3 here]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3. Curriculum architecture typology and typical Moore outcome depth\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Cross-Tabulation of Curriculum Architecture Types with the Highest Moore Outcome Level Achieved\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurriculum Architecture Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eL1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eL2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eL3a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eL3b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eL4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eL5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eType A: Stand-Alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eType B: Embedded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eType C: Longitudinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eType D: Model Proposal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e41\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: L1 = Participation; L2 = Satisfaction; L3a = Declarative Knowledge; L3b = Procedural Knowledge; L4 = Competence; L5 = Performance; NC = Not Classified. Cell values indicate the number of studies reporting outcomes at each level. Studies reporting multiple outcome levels were assigned to each applicable level; consequently, level totals exceed n = 41.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterprofessional training analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnly 6 of 41 studies (14.6%) explicitly targeted interprofessional learner groups. Cross-tabulation revealed that interprofessional curricula achieved higher representation at advanced Moore levels (L4–L5) than uniprofessional curricula. However, the small number of interprofessional studies limits generalizability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMost studies used pre–post designs to measure short-term changes, with some including additional data points to track persistence over time [21,23,32,37,49,50,59] and follow-up through retention assessments or multi-source ratings [27,32,54]. Simulation-based curricula often combined written assessments with performance evaluations such as checklists, triage accuracy metrics, and global rating scales [27,28,32,39,47,49,50,53–55]. Qualitative methods involved debriefing interviews, focus groups, and open-ended reflections [20,33,43,48]. Expert-focused methods included modified Delphi processes, curriculum mapping, and surveys of program directors [29–31,35,44].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 41 studies, data collection mostly used structured tools measuring knowledge, attitudes, confidence, preparedness, or satisfaction, using Likert scales and multiple-choice questions [19,22,23,36–38,40–42,46,47,51,52,57,59].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTraining outcomes using Moore's expanded outcomes framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of 41 studies, outcome reporting aligned with Moore's framework as follows: Level 1 (Reaction), 1 study (2.4%); Level 2 (Learning—knowledge and skills), 22 studies (53.7%); Level 3a (Learning—declarative knowledge), 16 studies (39.0%); Level 3b (Learning—procedural knowledge), 18 studies (43.9%); Level 4 (Transfer—behavior change), 15 studies (36.6%); Level 5 (Results—patient/system outcomes), 5 studies (12.2%); Not classified, 7 studies (17.1%) (Fig. 4).\u003c/p\u003e\n\u003cp\u003e[Figure 4 here]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4. Distribution of Moore outcome levels across the 41 included studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA key asymmetry emerged: Of 41 studies, 16 (39.0%) measured L3a (declarative knowledge) outcomes, but only 8 of these (19.5% of all studies) also measured L3b (procedural knowledge). This gap highlights the common focus on factual knowledge without a similar emphasis on assessing knowledge application in practice settings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFive studies achieved Level 5 outcomes: Kivlehan et al. (2021), Plaitano et al. (2021), Ripoll-Gallardo et al. (2020), Allen et al. (2016), and Bahattab et al. (2022). These studies cover multiple architecture types (A, B, C) and professional groups. Four studies were under-classified: Koziel et al. (2015), Cole et al. (2021), Park et al. (2024), and Varanelli et al. (2019), with outcomes that might warrant a Level 4 or higher designation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo curriculum has achieved formal measurement of Moore Level 6/7 (organizational change outcomes), highlighting a significant evidence gap caused by methodological constraints and the scarcity of curricula specifically focused on system redesign (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Distribution of Training Outcomes across Moore's Expanded Outcomes Framework\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMoore Outcome Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% of Studies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRepresentative Studies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel 1 — Participation/Reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel 2 — Satisfaction/Learning (self-report)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel 3a — Declarative Knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel 3b — Procedural Knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel 4 — Competence (behavior change)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel 5 — Performance (patient/system outcomes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKivlehan et al. 2021; Plaitano et al. 2021; Ripoll-Gallardo et al. 2020; Allen et al. 2016; Bahattab et al. 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel 6/7 — Patient/Community Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot Classified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Percentages sum to more than 100% as individual studies may report outcomes at multiple Moore levels. Studies classified at higher levels may also report lower-level outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFrameworks guiding training design, evaluation, and operational practice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTraining design frameworks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInstructional design frameworks and curriculum models varied among the included studies, although several did not report a guiding framework [20,29–31,35,50,57,59]. When described, approaches included competency-based and standards-driven designs such as outcome- and competency-based education models [42,43,54] and national or professional curricular standards [33,40]. Structured program frameworks like HSEEP and PennDemic [19,22] complemented experiential and adult-learner approaches, including andragogy [24,43], experiential learning, and clinical competence models [32,52], as well as cognitive or mastery-based methods [53,54]. Simulation-focused models, notably the Jeffries Simulation Model [47,52], and systematic design methods such as ADDIE and instructional systems design were also commonly used [27,44,53]. Study-specific approaches included quality improvement frameworks [21], flipped classroom delivery [51], game-based learning [38], backward curriculum design [49], disaster cycle structures [34,36], and disaster literacy models [41].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment frameworks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssessment approaches varied widely across studies, with many not specifying a formal evaluation framework [21,23–25,27,28,34,36,37,39,40,42–45,51,55,56,58,59]. When mentioned, evaluation methods included institutional processes like EFEE course evaluations [57] and HSEEP \"hot wash\" [19], as well as theory-based models such as Keller's ARCS Motivation Model [49] and frameworks focused on preparedness or competency [29,46,47,52]. Formal models encompassed Transfer of Learning, AACN Essentials [26], and Kirkpatrick-based evaluations combined with modified Delphi techniques [53,54]. Qualitative research references included established analytic standards [20,33,48]. Other methods covered KAB-style outcome measures, RACE-aligned behavior scales [38], TIPS and MASCAL/STX grading schemes [32], disaster literacy assessments based on the Health Belief Model [41], and outcome prioritization through Delphi approaches [35]. Two studies used guideline-informed content mapping to evaluate curricula [30,31].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOperational frameworks underpinning tabletop exercises\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncident management frameworks often serve as the foundation of curricula, typically referencing ICS and related all-hazards systems such as NIMS, HICS, and IMS [19,26,37,43,44,53,54,59]. Mass-casualty triage methods such as START and JumpSTART are commonly integrated [20,23,27,33,46,47,49,50,54–56,59], whereas variants such as SMART, SIEVE, SORT, and SALT are less frequently included [20,21,27,49]. The curricula also feature broader operational models, including the disaster management cycle and crisis standards of care [21,41,43,59], military trauma protocols like TCCC/DoD CPGs and ATLS [32,37,56], and specific procedures such as hazmat zoning [39], METHANE reporting [54], RACE fire response [38], and reverse triage techniques [26].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCognitive support tools and scaffolding approaches\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiple studies have detailed cognitive support tools or scaffolding strategies integrated into simulations, drills, or practical learning environments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlgorithm-based cognitive support has been examined in a limited number of studies. Koziel et al. integrated formal triage algorithms (SMART, START/JumpSTART) into simulations, supported by physical aids such as triage kits, reference cards, and length-based tapes, along with structured debriefings [20]. Shinchi et al. employed the SINCHI mnemonic framework, which included structured information-gathering prompts and checklist-style tasks within simulation exercises [58]. Wright et al. placed large SALT triage reference cards in clinical areas to enable point-of-care access [21]. Kim et al. utilized Emergo Train System materials, such as patient cards with clinical details and treatment cues, to assist decision-making [55].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMany studies reported the use of facilitator-mediated cognitive support. This included real-time cueing, guiding questions, or immediate feedback during or right after scenarios to help learners make decisions and reflect [32,39,47,53,54,56]. One study highlighted mentorship-based scaffolding, where novice participants worked one-on-one with experienced EMTs for real-time guidance [28]. Other methods involved START-based triage support [46], the RACE procedure [38], and access to triage algorithms during simulations [27]. Genç et al. described disaster information cards, mobile apps, and serious games with embedded cues and feedback [41], while Altman et al. reported using structured prompts from educators [34].\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eWhat This Review Contributes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis scoping review produces three main analytical outputs. We develop a taxonomy for disaster health curricula, categorizing 41 peer-reviewed studies based on architecture type (Stand-Alone, Embedded, Longitudinal, Model Proposal), professional discipline, and learner population. Additionally, we utilize Moore's expanded outcomes framework as a systematic analytical tool, highlighting significant gaps in outcome depth and showing that traditional Kirkpatrick classification masks important differences in competence assessment. Lastly, we identify two systemic challenges. The Declarative Knowledge Plateau and the Competence-Performance Gap that go beyond individual curricula and reflect broader limitations in how disaster health education is currently understood and evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMoore Outcome Analysis: The Moore Ceiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApplying Moore's framework to this literature highlights a clear outcome ceiling. While 53.7% of studies measured Kirkpatrick Level 2 (Learning), corresponding to Moore L2, only 12.2% reached Moore L5 (patient or system outcomes), and none achieved L6 or L7 (organizational change). This pattern exemplifies what we call the Moore Ceiling: a systematic upper limit where disaster health education outcomes remain largely unexplored and unmeasured. Such measurement limits are widely acknowledged in broader medical education.\u003c/p\u003e\n\u003cp\u003eTwo key phenomena explain this ceiling. The Declarative Knowledge Plateau, where 16 studies assessed declarative knowledge (L3a) but only eight moved on to measure procedural knowledge (L3b), indicates curricula effectively teach factual disaster response information (e.g., triage systems, incident command structures), but less focus is placed on translating this into practical skills under real conditions. This may be due to assessment choices or curricular gaps. Secondly, the Competence-Performance Gap shows a 66% drop from L4 (behavioral transfer, 15 studies) to L5 (patient or system outcomes, five studies). Even curricula that change practitioner behavior may not demonstrate impact on patients or systems. This gap likely results from the influence of multiple factors on clinical outcomes. While individual competence is necessary, it alone does not suffice; system factors, team dynamics, resources, implementation fidelity, organizational readiness, and health system resources all affect outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Curricular Coherence Gap\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis review revealed a Curricular Coherence Gap: a lack of systematic alignment among learning objectives, curriculum content, instructional methods, and assessment. About 60% of studies did not clearly connect their competency goals to specific modules or assessments. This disconnect indicates that many curricula, despite covering relevant disaster health skills, lack a coherent logic model that links content delivery, teaching strategies, and evaluations to desired outcomes. Improving curricular coherence could be a key factor in enhancing the depth of learning outcomes (Fig. 5).\u003c/p\u003e\n\u003cp\u003e[Figure 5 here]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 5. Curricular coherence: three-axis framework alignment across 41 studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCurriculum Architecture and Outcome Depth\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cross-tabulation of curriculum architecture with Moore outcome levels indicates a link between architecture type and outcome depth. Longitudinal curricula (Type C), although only 9.8% of the sample, showed a higher proportion in L4–L5 outcomes (50% compared to the mean of 36.6%), based on four studies. This pattern suggests a need for future research, though the small sample size limits causal conclusions. Conversely, Stand-Alone curricula (Type A), which are often published and easy to implement immediately, mainly focus on L1–L2 outcomes. Embedded curricula (Type B) fell in between, with outcomes spread across L2–L4 and some L5. These findings imply that features such as duration, integration, and iterative refinement could enhance the potential of the outcome. Our curriculum architecture typology can also be related to Harden's Integration model, which describes a spectrum from discipline-specific to fully integrated curricula. Our typology aligns this concept with implementation timeframes and levels of institutional embedding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Interprofessional Training Paradox\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Interprofessional Training Paradox highlights a key structural challenge. Effective mass casualty response depends on smooth coordination among various professions, yet only 14.6% of disaster health curricula explicitly focus on interprofessional groups. This paradox might indicate publication bias, a real lack of such programs, or their existence mainly in gray literature. The six interprofessional studies showed a higher proportion of outcomes at levels L4–L5, suggesting that interprofessional design could lead to better transfer and better outcomes. Nonetheless, with only six studies, confidence in these outcome links is limited. We emphasize this as a research priority rather than a confirmed practice gap.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCore Competencies and Cognitive Support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross curricula, triage (85.4%, 35 studies) and incident command systems (68.3%, 28 studies) were identified as nearly universal competency targets. These address the key challenges of mass casualty management: fairly distributing scarce resources and coordinating response teams spread out across different locations. However, few curricula explicitly assessed the integration of these skills; most evaluated them separately, which may hinder effective decision-making when clinical and logistical demands occur simultaneously. Cognitive support tools, such as mnemonics, decision trees, job aids, checklists, and scenario-based simulations, are a secondary focus across curricula, helping reduce cognitive load during complex, high-pressure decisions. The five-category taxonomy identified—comprising Mnemonic Aids, Algorithmic Supports, Job Aids and Checklists, Scenario Scaffolding, and Mentoring/Team Support—includes various methods that externalize decision logic to maintain performance under stress. This exploratory taxonomy, derived inductively, needs validation against established frameworks in cognitive load theory. Curricula that incorporate multiple scaffolding strategies within cohesive educational structures tend to produce better outcomes, indicating possible synergistic effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Directions and Recommendations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe offer five interconnected recommendations, each addressing a dimension of the identified evidence gaps.\u003c/p\u003e\n\u003cp\u003eCurriculum Design: Future disaster health curricula should incorporate clear logic models that connect competency objectives with content, teaching methods, and assessment of outcomes. Whenever possible, long-term structures should be emphasized, as existing evidence indicates that sustained engagement improves the depth of outcomes.\u003c/p\u003e\n\u003cp\u003eInterprofessional Integration: Curriculum developers should intentionally create programs that include mixed professional teams, mirroring real-world mass-casualty response scenarios. Additionally, funding and publication incentives should be aligned to encourage the development and evaluation of interprofessional curricula.\u003c/p\u003e\n\u003cp\u003eOutcome Measurement: Evaluation strategies should focus on assessing outcomes at the highest level feasible within Moore's framework, and provide a clear justification if only levels L1–L3 are measured. For curricula deployed in the field, Moore L4 (transfer) should be the standard; for significant curriculum innovations, L5 (patient/system outcomes) should be given priority. This framework highlights outcome types often not fully captured by traditional assessments, such as skill decay, retention trajectories, team performance metrics, and organizational adoption of curricula.\u003c/p\u003e\n\u003cp\u003eCurricula should systematically include a range of cognitive support strategies within a unified framework that covers both clinical and command decision-making. Research in implementation science should explore which combinations of scaffolding are most effective and identify the contextual factors that influence their success.\u003c/p\u003e\n\u003cp\u003eEvidence Synthesis and Dissemination: The field needs ongoing systematic reviews that regularly update evidence regarding curriculum effectiveness, architecture types, and outcome trends. Discipline-specific societies should develop competency-based curriculum frameworks to steer future growth and ensure consistent measurement across programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis review recognizes several key limitations. Firstly, assigning studies to Moore's outcome levels involved interpretive judgment about competence and behavior change, with the distinction between L3a (declarative competence) and L3b (procedural competence) requiring subjective interpretation. Future reviews could adopt more standardized outcome reporting standards to minimize ambiguity. Moreover, the assessment of curriculum coherence was qualitative, based solely on the authors' review of published descriptions, and lacked a systematic framework, limiting reproducibility.\u003c/p\u003e\n\u003cp\u003eSecond, many included studies did not clearly specify all curriculum components or the methods used to measure outcomes, likely due to limited publication space. Only two studies reported protocol deviations, suggesting that such deviations may be infrequent or underreported. There may also be publication bias favoring successful implementations, leading to an overestimation of the actual effectiveness.\u003c/p\u003e\n\u003cp\u003eThird, the typology of curriculum architecture and the taxonomy of cognitive support tools were developed inductively from study characteristics and content analysis, respectively. Both serve as exploratory classifications whose validity, usefulness, and completeness for future comparative research still need to be confirmed.\u003c/p\u003e\n\u003cp\u003eThis review primarily examined peer-reviewed English-language publications since 2013, which means it may have overlooked important innovations documented in gray literature, non-English sources, or local reports. Following scoping review methodology, we did not perform a formal critical appraisal of the included studies, so we could not assess the evidence's strength and quality, nor draw definitive conclusions about effectiveness. The considerable variation across studies in populations, contexts, training methods, and outcome measures made direct comparisons difficult and precluded quantitative analysis. Most studies employed quasi-experimental, pre–post, or descriptive designs with limited follow-up, which constrained insights into long-term behavioral change and broader system impacts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis scoping review of 41 peer-reviewed disaster health curricula reveals a maturing field with widespread adoption of evidence-based practices but persistent gaps in evidence at deeper levels of outcome measurement. Applying Moore's expanded outcomes framework uncovers the Moore Ceiling: a systematic limitation in how thoroughly curricula evaluate competence and behavior change. Three key challenges. The Declarative Knowledge Plateau, the Competence-Performance Gap, and the Curricular Coherence Gap highlight actionable areas for improvement. The low prevalence of interprofessional curricula (14.6% of the sample) indicates both a research gap and a practice necessity, given the inherently interprofessional nature of mass casualty response.\u003c/p\u003e\n\u003cp\u003eAdvancing deeper outcome measurement requires curriculum developers to implement clear logic models, prioritize longitudinal structures where feasible, and systematically include interprofessional design. The five studies demonstrating Moore L5 outcomes demonstrate that patient- and system-level impacts can be measured. Achieving this shift depends on aligning funding strategies with interprofessional curriculum development and establishing discipline-specific competency frameworks to unify outcome measurement across programs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMCI: Mass Casualty Incident; MCI-PHER: Mass-Casualty Incident \u0026mdash; Prehospital Emergency Response; WHO: World Health Organization; EMS: Emergency Medical Services; PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews; PRESS: Peer Review of Electronic Search Strategies; ICS: Incident Command System; NIMS: National Incident Management System; HICS: Hospital Incident Command System; IPE: Interprofessional Education; CBRN: Chemical, Biological, Radiological, and Nuclear; START: Simple Triage and Rapid Treatment; SMART: Sieve, Military, Assess, Respiratory, Treatment; SALT: Sort, Assess, Lifesaving Interventions, Treatment/Transport; OSCE: Objective Structured Clinical Examination; EMAP: Emergency Management Accreditation Program; ICRC: International Committee of the Red Cross; MSF: M\u0026eacute;decins Sans Fronti\u0026egrave;res; LMICs: Low- and Middle-Income Countries; ADDIE: Analysis, Design, Development, Implementation, Evaluation; HSEEP: Homeland Security Exercise and Evaluation Program; KAB: Knowledge, Attitudes, and Behavior; EMDM: European Master in Disaster Medicine; EMT: Emergency Medical Technician\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Dr Sarah Kazim, Chair of Emergency Medicine at Dubai Health, for her support and for fostering the departmental environment that helped make this work possible. They also thank Mohammed Bin Rashid University of Medicine and Health Sciences for its support and collaboration in this research, Shakeel Tegginmani of the Al Maktoum Medical Library, for assistance with the literature search, and the Institute of Learning (IOL), for research support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;N.M.A: Data curation, Investigation, Methodology, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. A.O: Conceptualization, Data curation, Methodology, Formal analysis, Supervision, Writing \u0026ndash; review \u0026amp; editing. S.A.E: Data curation, Investigation, Methodology, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. M.A: Methodology, Writing \u0026ndash; review \u0026amp; editing. H.H.Y: Investigation, Resources, Data curation, Writing \u0026ndash; review \u0026amp; editing. A.Y: Conceptualization, Project administration, Resources, Supervision, Writing \u0026ndash; review \u0026amp; editing. I.H: Supervision, Methodology, Writing \u0026ndash; review \u0026amp; editing. N.Z: Conceptualization, Visualization, Supervision, Methodology, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Mohammed Bin Rashid University of Medicine and Health Sciences through the Dubai Health Collaborative Stimulus Research Grant (CSRG) for the M-CIPHER project. The funding organization had no influence on the study design, data analysis, or manuscript preparation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its additional files. The review protocol is available on Protocols.io.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This scoping review synthesized only previously published, publicly available literature and involved no direct contact with human participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Mass casualty management systems 1. World Health Organization. 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Public Health Nurs. 2020;37(2):287\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/phn.12685\u003c/span\u003e\u003cspan address=\"10.1111/phn.12685\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mass casualty incident, Disaster medicine, Curriculum design, Healthcare education, Scoping review, Moore's outcomes framework, Interprofessional education, Medical education","lastPublishedDoi":"10.21203/rs.3.rs-9230758/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9230758/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eResponding effectively to mass casualty incidents (MCIs) depends on well-trained healthcare professionals. However, there is no comprehensive synthesis that systematically maps how disaster and MCI training curricula are created, implemented, and assessed across various healthcare roles worldwide.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe performed a scoping review guided by the Arksey and O'Malley framework and adhered to PRISMA-ScR guidelines. Seven databases: PubMed, Embase, Scopus, PsycINFO, CINAHL, Cochrane Library, and ClinicalTrials.gov were searched for English-language studies published from 2015 to 2025. Two reviewers independently screened articles and extracted data. We mapped training outcomes using Moore's Expanded Outcomes Framework (Levels 1\u0026ndash;7) and categorized studies based on curriculum architecture type. Additionally, we developed three original analytical constructs: a Curriculum Architecture Typology, a Curricular Coherence Analysis, and a Cognitive Scaffolding Taxonomy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eForty-one studies met the inclusion criteria, representing various geographical regions, learner groups, and study designs. The curricula fell into four main types: Stand-Alone (n\u0026thinsp;=\u0026thinsp;14), Embedded (n\u0026thinsp;=\u0026thinsp;16), Longitudinal (n\u0026thinsp;=\u0026thinsp;4), and Model Proposal (n\u0026thinsp;=\u0026thinsp;7). Evaluation of outcomes showed a Moore Ceiling: 53.7% of studies measured satisfaction (L2), but only 12.2% evaluated performance outcomes (L5), and none achieved patient or community health outcomes (L6/L7). Most studies focused solely on factual knowledge (16 studies) and did not assess procedural knowledge (8 studies), highlighting a Knowledge Depth Gap. Interprofessional curricula were uncommon, with only 6 of 41 studies (14.6%) explicitly involving interprofessional learner groups, despite MCIs requiring coordinated team responses. Longitudinal curricula reached deeper Moore outcome levels more often than stand-alone formats.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis review highlights significant gaps in outcomes depth, interprofessional integration, and curricular coherence across the field. Future curricula should focus on longitudinal structures, intentional interprofessional design, more comprehensive outcome measurement, and systematic cognitive scaffolding to improve disaster preparedness education.\u003c/p\u003e","manuscriptTitle":"Curriculum Design in Disaster Medicine and Mass Casualty Incident Training: A Scoping Review of Healthcare Education","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-26 15:32:24","doi":"10.21203/rs.3.rs-9230758/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T07:15:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T19:24:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235861915499540213396358683924613228271","date":"2026-04-30T19:18:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T19:24:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275535826712710871735774541149655742859","date":"2026-04-16T09:11:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-16T08:41:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-31T06:51:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-30T03:29:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-30T03:29:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-03-26T07:26:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c327921e-3276-413d-8969-9d58df4f4ed6","owner":[],"postedDate":"April 26th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T07:15:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T19:24:44+00:00","index":46,"fulltext":""},{"type":"reviewerAgreed","content":"235861915499540213396358683924613228271","date":"2026-04-30T19:18:10+00:00","index":45,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T07:25:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-26 15:32:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9230758","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9230758","identity":"rs-9230758","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}