Development of a Middle-Range Theoretical Framework for Low- Cost Simulators in Clinical Simulation

Development of a Middle-Range Theoretical Framework for Low- Cost Simulators in Clinical Simulation | 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 Development of a Middle-Range Theoretical Framework for Low- Cost Simulators in Clinical Simulation Raphael Raniere de Oliveira Costa, Marília Souto de Araújo, Soraya Maria de Medeiros, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9364818/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Simulation-based education is widely used to build clinical competence in healthcare education. Low-cost simulators are increasingly adopted, especially in resource-limited and sustainability-focused settings. However, limited theoretical frameworks restrict systematic evaluation and broad implementation. This study aimed to develop a middle-range theoretical framework explaining the use of low-cost simulators in clinical simulation. Methods Walker and Avant’s theory construction method was used to define core domains, measurable constructs, and testable propositions, supported by previous concept analysis and literature synthesis. Results The framework comprises four domains: Antecedents (contextual variables describing the conditions that justify and enable use of low-cost simulators, such as educational need, institutional infrastructure, and equity-related access constraints); Implementation (process variables describing how low-cost simulation is applied, operationalized through simulator attributes including affordability, access feasibility, and contextual adaptability); Modulators (modulating variables influencing implementation quality and consistency, such as instructional alignment and pedagogical strategy, faculty competence and qualifications, and learner readiness); and Consequences (outcome variables), including proximal outcomes (access to clinical simulation and practice-based learning, learning and competence development, efficiency and sustainability) and a distal outcome (improved quality of healthcare delivery). Thirteen propositions specifying relationships among these domains were formulated to support future empirical testing. Conclusions The proposed framework provides an operational guide to evaluate, implement, and scale low-cost simulation in nursing and other health professions education. Simulation Training Education Nursing Clinical Competence Models Educational Sustainable Development Figures Figure 1 INTRODUCTION Simulation-based education is increasingly used in healthcare education to develop clinical competence in safe learning environments 1 . Across healthcare education, simulation is recognized as a foundation of patient safety and quality of care, and it supports supervised acquisition of technical and non-technical skills, lessening reliance on learning exclusively in real clinical situations 2 , 3 . Despite its value, scaling simulation remains difficult due to infrastructure needs, high costs, and a shortage of specialized staff; implementation often requires considerable investment in technology, faculty development, and the adaptation of educational resources 4 . These constraints are most evident in resource-limited settings with restricted access to commercial simulators and in programs prioritizing sustainability, where cost, maintenance, and long-term feasibility are central considerations. In reaction to these challenges, low-cost simulators (LCS) have appeared as accessible, reproducible, and sustainable alternatives 5 . Built from alternative materials and spanning different levels of technological complexity, LCS support clinical skills development in areas such as surgery, pediatric oncology, and ultrasound 6 – 8 . Their adoption is consistent with calls to evaluate educational innovations through a sustainability lens, emphasizing scalability and long-term feasibility in health and education systems 9 , 10 . However, despite growing adoption of LCS, the theoretical foundation supporting their implementation remains underdeveloped. Much of the existing work relies on fidelity-driven assumptions and provides limited guidance on the specific conditions in which these simulators generate consistent educational benefits. Current debates in simulation research center on the assumption that greater technological fidelity necessarily produces better learning outcomes, whereas emerging evidence suggests that pedagogical alignment and institutional capacity may be more decisive. Without a coherent theoretical structure, LCS risks being positioned as ad hoc substitutes rather than legitimate, evidence-based simulation tools. Developing a middle-range theoretical framework (MRTF) organizes existing knowledge on LCS and delivers the conceptual structure required for systematic evaluation. Middle-range theories explain specific phenomena and support empirical testing by linking theory to observable practice 11 . This study develops an MRTF for low-cost simulation in healthcare education to clarify essential elements, specify theoretically grounded relationships, and align low-cost strategies with high-stakes educational goals. The proposed MRTF provides educators and researchers with a foundation to expand equitable access and drive quality improvement in simulation-based education, directly addressing the outlined gaps. METHODS An inductive theory-construction study guided by Walker and Avant’s method 12 was conducted in seven stages. The first stage identified the central concept of low-cost simulation, supported by a previously published integrative review 5 across eight databases. The second stage specified the concept’s essential elements to distinguish LCS from related educational tools. The third stage articulated the theory’s purpose to explain the phenomenon and guide its application. The fourth stage identified dimensions and variables to establish the model’s conceptual architecture. The fifth stage identified antecedents and consequences to clarify conditions of use and practical impact. The sixth stage formulated the theory through propositions that linked the concept to related factors and aligned low-cost technology with high-stakes educational goals. The seventh stage evaluated the theory by assessing internal consistency and logical robustness to support subsequent empirical testing. This study did not involve human participants or data collection and, therefore, did not require institutional review board approval. RESULTS Stages 1, 2, and 3: Identification, Definition, and Purpose of the Concept The central concept is the use of low-cost simulators in clinical simulation (LCS-CS) within healthcare education. Their use has expanded, particularly in settings where financial and infrastructural constraints restrict access to commercial devices. However, the concept remains inconsistently defined across the literature, complicating comparative research, curriculum planning, and standardization of simulation interventions. Accordingly, systematic concept clarification was undertaken before theory development. LCS are defined as “simulation devices used for clinical skills training and simulation-based education, developed or acquired at a lower cost than comparable commercial models, generally characterized by accessible materials, feasibility of assembly, and replicability, and may include different levels of technological complexity, depending on the intended educational purpose”. This concept analysis clarified the meaning and boundaries of LCS-CS, thereby supporting its operationalization within the proposed framework. A recurrent source of ambiguity was the interchangeable use of the terms low-cost and handmade. Here, low-cost refers to affordability and access feasibility relative to context and comparable commercial alternatives, whereas handmade refers to the production mode. This distinction matters because handmade devices may not remain low-cost over time when durability and replacement costs are considered. Across identified uses, LCS were described in relation to technological complexity, production mode, and relative cost, informing the framework’s classification approach. Concept clarification also informed the framework’s implementation attributes, defined in Table 1: affordability, access feasibility, sustainability capacity, manufacturability and assembly feasibility, portability, purpose-appropriate fidelity, enabling technology integration, replicability and consistency, versatility, and contextual adaptability. Table 1. Low-Cost simulator attributes and definitions Stage 4: Identification of Dimensions and Variables Measurable variables were grouped into four domains to enable empirical testing of pathways from context through implementation and modulators to outcomes. Contextual variables (antecedents) External conditions that define the background conditions within which LCS may be considered a viable and desirable educational solution. These include: (ANT1) educational need, referring to demand for competency-based clinical training supported by safe, structured, and reproducible learning experiences; (ANT2) availability of commercial simulation models, describing the extent to which commercially produced and standardized simulation equipment is accessible to an institution; (ANT3) institutional infrastructure, referring to baseline physical and technical capacity (e.g., availability of simulation spaces, general equipment, and maintenance resources); (ANT4) equity-related access constraints, describing socioeconomic barriers affecting access to educational resources and participation in simulation-based education; (ANT5) educational policies, including regulatory and curricular requirements that govern teaching approaches and assessment expectations; and (ANT6) institutional commitment to sustainability, reflected in strategic priorities and procurement policies that focus on reuse, repairability, and environmentally responsible practices. Process variables (implementation) Implementation refers to how LCS-CS is applied. This domain encompasses both the delivery of simulation-based learning experiences and the technical and functional characteristics of the simulators that support these activities. Accordingly, implementation is operationalized through a set of measurable simulator attributes (SA) with operational definitions provided in Supplemental Digital Content 1. These attributes enable consistent characterization and comparison of LCS across educational contexts and facilitate systematic evaluation of implementation. Modulating variables (modulators) Instructional and institutional conditions associated with variation in LCS-CS implementation quality and consistency. These include: (MOD1) instructional alignment and pedagogical strategy, encompassing accordance with educational objectives and the teaching approaches adopted (e.g., deliberate practice, feedback processes, debriefing structure, and integration within wider teaching plans); (MOD2) faculty competence and qualifications, describing educators’ preparation and competence in simulation facilitation, debriefing, and assessment; (MOD3) learner readiness, reflecting learners’ baseline experience level, prior exposure, as well as readiness for simulation-based activities; and (MOD4) institutional capacity for implementation, encompassing institutional culture of innovation and available technical and pedagogical support, including simulation-specific infrastructure, staff support, and organizational arrangements that enable ongoing delivery. Outcome variables (consequences) Expected effects of LCS-CS within healthcare education. These include: (PO1) access to clinical simulation, reflected in expanded opportunities for participation in simulation-based learning; (PO2) learning and competence development, encompassing the development and consolidation of competencies and skills, including technical and non-technical performance; (PO3) instructional quality and teaching capacity, representing the quality of implementation (instructional design, facilitation, debriefing, and learner assessment) and the refinement of teaching practices enabled by low-cost simulation; and (PO4) efficiency and sustainability, including resource utilization and cost-related indicators (e.g., reduced reliance on consumable materials), reuse of materials, and the sustained viability of simulation programs supported by low-cost solutions. The distal outcome is healthcare quality (DO1), conceptualized as downstream improvement associated with sustained and effective LCS-CS over time. Stage 5: Formulation of the Theoretical Proposition The MRTF proposes that, under antecedent conditions including educational need (ANT1), limited availability of commercial simulation models (ANT2), constrained institutional infrastructure (ANT3), equity-related access constraints (ANT4), educational policies (ANT5), and institutional commitment to sustainability (ANT6), the use of low-cost simulators in clinical simulation (LCS-CS) becomes a feasible and context-responsive educational strategy. Implementation is operationalized through simulator attributes (SA1–SA10) that determine feasibility, functionality, scalability, and fit-for-purpose. However, these attributes alone are insufficient to ensure consistent educational gains. The extent to which LCS-CS produces proximal outcomes, access to clinical simulation and practice-based learning (PO1), learning and competence development (PO2), instructional quality and teaching capacity (PO3), and efficiency and sustainability (PO4) depends on key modulators, including instructional alignment and pedagogical strategy (MOD1), faculty competence and qualifications (MOD2), learner readiness (MOD3), and institutional capacity for implementation (MOD4). Over time, sustained improvements in these proximal outcomes are expected to contribute to the distal outcome of improved quality of healthcare delivery (DO1). Stage 6: Theoretical Model Stage 6 formalized the MRTF as a theoretical model by integrating the framework domains into a coherent explanatory structure. Figure 1 graphically presents the proposed MRTF model, depicting the hypothesized relationships among antecedents, simulator attributes, modulators, and outcomes, while Table 2 specifies these relationships through a set of testable propositions. Figure 1. Middle-range theoretical framework for the use of low-cost simulators in healthcare education: a graphic representation. Table 2. Thematic axes and theoretical propositions of the middle-range theoretical framework for the use of low-cost simulators in healthcare education. Stage 7: Empirical Testing Empirical testing of the framework involves systematically observing and measuring its constructs and propositions. Stage 7 was not conducted in the present study because the primary aim was theory construction and conceptual specification. Accordingly, empirical testing is positioned as the next step in the research program and will be addressed in future studies. As identified in the introduction and summarized in the Supplemental Digital Content 2, current empirical evidence in the low-cost simulation literature is concentrated on simulator attributes (SA) and feasibility, and, in some cases, extends to proximal outcomes, particularly learning and competence development (PO2) and instructional quality and teaching capacity (PO3). In contrast, there is substantially less evidence explicitly examining modulators as causal conditions, especially instructional alignment and pedagogical strategy (MOD1) and institutional capacity for implementation (MOD4), measured through formal constructs. Evidence regarding the distal outcome of improved quality of healthcare delivery (DO1) remains minimal, as expected given its longer causal trajectory and the methodological demands of longitudinal evaluation. This distribution of evidence provides a diagnostic snapshot of the field and motivates future empirical testing. As part of the empirical literature, the Instrument for the Evaluation of Low-Cost Simulators 13 was developed and content validated to assess the presence of simulator attributes aligned with this framework. The instrument includes 25 items across six factors that operationalize SA1–SA10: “Cost” includes items related to affordability (SA1); “Accessibility” captures access feasibility (SA2) and portability (SA5); “Technology, manufacturing and reproducibility” captures manufacturability and assembly feasibility (SA4), enabling technology integration (SA7), replicability and consistency (SA8), and sustainability capacity (SA3); “Realism” reflects fidelity (SA6); “Versatility” reflects versatility (SA9) and contextual adaptability (SA10); and “Usability” captures deployment and handling indicators that overlap with SA2, SA4, SA5, and contextual adaptability (SA10). DISCUSSION The proposed MRTF advances theory-informed LCS-CS within healthcare education, particularly in resource-limited environments. By integrating simulator attributes, contextual conditions, implementation modulators, and proximal and distal outcomes within a single explanatory structure, the framework supports systematic planning and evaluation of LCS-CS and enables cumulative, theory-driven empirical testing. The MRTF is consistent with established simulation theories and standards while addressing a specific gap related to low-resource decision pathways. The Jeffries Simulation Theory 14 emphasizes instructional design, learning outcomes, and faculty preparation, but it does not explicitly represent the constraints, trade-offs, and feasibility decisions that shape simulation design and delivery when commercial resources and institutional infrastructure are limited. By specifying antecedent conditions, simulator attributes as implementation constructs, and modulators that shape implementation quality and the translation of LCS-CS into outcomes, the MRTF extends this foundation for contexts where sustainability and equity are central priorities. A key contribution of the framework is its shift from device-centered explanations toward an implementation-focused account of effectiveness. Rather than treating LCS performance as a direct function of simulator design or technological sophistication, the MRTF specifies the conditions under which LCS-CS is likely to generate meaningful educational and organizational benefits and the mechanisms by which it is likely to do so. From this perspective, simulator attributes (SA1–SA10) are necessary but not sufficient. Their educational value depends on the strength of modulators, particularly instructional alignment and pedagogical strategy (MOD1), faculty competence and qualifications (MOD2), learner readiness (MOD3), and institutional capacity for implementation (MOD4). This emphasis positions implementation quality and contextual fit as key explanatory drivers and reframes fidelity as purpose-appropriate rather than technology-determined. This emphasis also contributes to ongoing debates in simulation research, including the assumption that higher technological fidelity necessarily produces superior outcomes. The MRTF conceptualizes effectiveness as emerging from the alignment among simulator functionality, scenario design, and pedagogical strategy. Accordingly, low-technology solutions may yield comparable outcomes when learning objectives, instructional alignment, and facilitation practices are coherent and adequately supported. The MRTF also provides a practical planning logic for educators and institutions. For example, in a resource-limited healthcare program intending to strengthen intravenous cannulation, educators may first recognize persistent performance gaps in assessment and clinical placement (ANT1), restricted access to commercial devices (ANT2), and limited simulation space (ANT3). They respond by developing or selecting LCS that can be deployed at scale, prioritizing affordability (SA1), access feasibility (SA2), manufacturability and assembly feasibility (SA4), replicability and consistency (SA8), and purpose-appropriate fidelity (SA6) aligned with learning objectives. To ensure that these attributes translate into learning, sessions are embedded within a structured teaching plan with well-defined objectives and brief, standardized debriefing (MOD1), and faculty are prepared to provide feedback and facilitate practice using consistent criteria (MOD2). Learners complete short preparatory activities before hands-on sessions and progress through staged practice to ensure readiness (MOD3), while the institution provides protected timetables, basic technical support, and a simple system for storage, distribution, and replacement of components (MOD4). Implementation is then tracked through increased participation and practice opportunities (PO1), improved technical and non-technical performance in skills assessments (PO2), more consistent facilitation and debriefing quality across groups (PO3), and reduced costs per learner alongside more efficient reuse and longer-term feasibility (PO4). Consistent improvements over successive cohorts provide a plausible pathway to downstream gains in healthcare quality (DO1). Conformity with the Healthcare Simulation Standards of Best Practice 15 further supports the framework’s relevance for implementation. The modulators domain is consistent with standards emphasizing facilitation, debriefing, and outcomes-driven design, reinforcing that learning quality depends on structured preparation and reflection processes rather than technological sophistication alone. The institutional capacity construct (MOD4) also concurs with operational standards that require planning, staffing, and governance to support safe and sustainable delivery, including in low-cost contexts. Finally, the outcomes domain emphasizes measurable educational and organizational effects (PO1–PO4) while situating improved quality of healthcare delivery (DO1) as a longer-term outcome requiring longitudinal research designs. The framework also makes explicit why equity and sustainability should be treated as implementation-relevant constructs rather than aspirational goals. By operationalizing affordability, access feasibility, and sustainability capacity alongside contextual constraints, the MRTF supports decision-making that responds to resource limitations while preserving educational quality. This positioning supports curriculum and institutional planning to guarantee equitable access to simulation-based learning opportunities and sustainable delivery models. This study has several limitations. First, the concept analysis and literature synthesis reflect the current state of the field, which shows heterogeneity in reporting and outcome measurement. As a result, some constructs, particularly modulators, are less consistently described in the empirical literature, limiting the extent to which they could be anchored to standardized indicators at this stage. Second, although the framework specifies a distal outcome, evidence linking LCS-CS to healthcare delivery outcomes is scarce and methodologically demanding. Improved quality of healthcare delivery should therefore be interpreted as a long-trajectory target mediated by sustained proximal improvements rather than an immediate effect. Third, the MRTF was developed through theory construction and conceptual specification, so Stage 7 empirical testing was not conducted. Although the framework is operationalized through measurable constructs and propositions, its explanatory pathways require prospective evaluation throughout various contexts. Finally, the model is intended to be broadly suitable across healthcare education settings, yet variation in curricular structures, regulatory requirements, and institutional infrastructure may affect implementation pathways, pointing to the need for multisite testing and refinement. Further investigations should prioritize empirical testing using designs that systematically observe and measure the framework constructs and propositions across diverse educational contexts. Current low-cost simulation research is concentrated on simulator attributes and feasibility and only partially addresses proximal outcomes, whereas modulators, particularly instructional alignment and pedagogical strategy (MOD1) and institutional capacity for implementation (MOD4), are less often operationalized as causal conditions; evidence for DO1 also remains limited given its distal and longitudinal nature. As an initial step, pilot and feasibility studies should operationalize antecedent conditions, simulator attributes, and modulators as measurable indicators using standardized measures, with particular emphasis on MOD1 and MOD4. Subsequent multisite studies should test key pathways linking implementation to proximal outcomes using comparable metrics and mixed method approaches that capture both educational effectiveness and implementation quality. Prospective designs are also needed to examine distal outcomes over longer causal trajectories. Collectively, this roadmap supports cumulative, theory-driven evidence generation to refine the MRTF and identify the most testable propositions across contexts. CONCLUSION This study developed an MRTF that clarifies how LCS-CS can expand simulation-based education in healthcare while supporting sustainability and equity goals. By specifying contextual antecedents, simulator attributes, modulators of implementation quality, and proximal and distal outcomes, the framework provides an operational basis for systematic planning, evaluation, and cumulative research. The framework conceptualizes LCS not as a purely technical or economic solution but as an implementation strategy whose value depends on the interaction between simulator characteristics and pedagogical and institutional conditions. Consistent educational gains are more likely when purpose-appropriate fidelity and implementation rigor are supported through instructional alignment and pedagogical strategy, faculty competence, learner readiness, and institutional capacity. By operationalizing simulator attributes as measurable constructs, the MRTF supports decisions that respond to resource constraints while preserving educational quality and provides a theory-informed basis for curriculum design, institutional strategy, and policy discussions directed at equitable and sustainable expansion of simulation-based learning opportunities. Finally, by providing shared conceptual language and explicit propositions, the MRTF supports a shift from isolated feasibility demonstrations to systematic, theory-driven testing and implementation. Over time, this approach can strengthen the evidence base needed to evaluate pathways from proximal educational and organizational outcomes to distal improvements in healthcare delivery. Declarations Human Ethics and Consent to Participate: Not applicable. Conflicts of interest/Competing interests: The authors declare no competing interests. Availability of data and material: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Funding: Not applicable. Declarations, Ethics approval, and consent to participate: Not applicable. Consent for publication: Not applicable. Authors' contributions: All authors reviewed and approved the final version submitted for publication. References Shin S, Park JH, Kim JH. Effectiveness of patient simulation in nursing education: Meta-analysis. Nurse Educ Today. 2015;35(1):176–82. 10.1016/j.nedt.2014.09.009 . Lioce L, Lopreiato J, Anderson M, Deutsch ES, Downing D, Robertson J. Healthcare Simulation Dictionary. 3rd ed. Agency for Healthcare Research and Quality; 2020. Nippita S, Haviland MJ, Voit SF, Perez-Peralta J, Hacker MR, Paul ME. 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Watts PI, Rossler K, Bowler F, et al. Onward and Upward: Introducing the Healthcare Simulation Standards of Best PracticeTM. Clin Simul Nurs. 2021;58:1–4. 10.1016/j.ecns.2021.08.006 . Tables Table 1 to 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table 1. Low-Cost simulator attributes and definitions Table2.docx Table 2. Thematic axes and theoretical propositions of the middle-range theoretical framework for the use of low-cost simulators in healthcare education. Cite Share Download PDF Status: Posted Version 1 posted 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. 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representation.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure.png","url":"https://assets-eu.researchsquare.com/files/rs-9364818/v1/9cd915d8d6a5d8e7378ccd96.png"},{"id":109166079,"identity":"88a4e3d2-1355-4177-b39a-0ff0e461d405","added_by":"auto","created_at":"2026-05-13 08:16:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":421279,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9364818/v1/c4692c41-80da-4e04-9745-a3d2079c6d93.pdf"},{"id":108976435,"identity":"edb4b48a-bae8-4443-b21e-fe3089fd8b72","added_by":"auto","created_at":"2026-05-11 11:20:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16007,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e\u003cem\u003e Low-Cost simulator attributes and definitions\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9364818/v1/b06d378acd3160ecc3f6cd05.docx"},{"id":108181546,"identity":"702e97f2-a11e-4a01-be2e-2eec113e871a","added_by":"auto","created_at":"2026-04-30 08:58:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16454,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 2. \u003c/strong\u003e\u003cem\u003eThematic axes and theoretical propositions of the middle-range theoretical framework for the use of low-cost simulators in healthcare education.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9364818/v1/2240db251e7b329b2ce1942f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDevelopment of a Middle-Range Theoretical Framework for Low- Cost Simulators in Clinical Simulation\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSimulation-based education is increasingly used in healthcare education to develop clinical competence in safe learning environments\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Across healthcare education, simulation is recognized as a foundation of patient safety and quality of care, and it supports supervised acquisition of technical and non-technical skills, lessening reliance on learning exclusively in real clinical situations\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Despite its value, scaling simulation remains difficult due to infrastructure needs, high costs, and a shortage of specialized staff; implementation often requires considerable investment in technology, faculty development, and the adaptation of educational resources\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. These constraints are most evident in resource-limited settings with restricted access to commercial simulators and in programs prioritizing sustainability, where cost, maintenance, and long-term feasibility are central considerations.\u003c/p\u003e \u003cp\u003eIn reaction to these challenges, low-cost simulators (LCS) have appeared as accessible, reproducible, and sustainable alternatives\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Built from alternative materials and spanning different levels of technological complexity, LCS support clinical skills development in areas such as surgery, pediatric oncology, and ultrasound\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Their adoption is consistent with calls to evaluate educational innovations through a sustainability lens, emphasizing scalability and long-term feasibility in health and education systems\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, despite growing adoption of LCS, the theoretical foundation supporting their implementation remains underdeveloped. Much of the existing work relies on fidelity-driven assumptions and provides limited guidance on the specific conditions in which these simulators generate consistent educational benefits. Current debates in simulation research center on the assumption that greater technological fidelity necessarily produces better learning outcomes, whereas emerging evidence suggests that pedagogical alignment and institutional capacity may be more decisive. Without a coherent theoretical structure, LCS risks being positioned as ad hoc substitutes rather than legitimate, evidence-based simulation tools.\u003c/p\u003e \u003cp\u003eDeveloping a middle-range theoretical framework (MRTF) organizes existing knowledge on LCS and delivers the conceptual structure required for systematic evaluation. Middle-range theories explain specific phenomena and support empirical testing by linking theory to observable practice\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This study develops an MRTF for low-cost simulation in healthcare education to clarify essential elements, specify theoretically grounded relationships, and align low-cost strategies with high-stakes educational goals. The proposed MRTF provides educators and researchers with a foundation to expand equitable access and drive quality improvement in simulation-based education, directly addressing the outlined gaps.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eAn inductive theory-construction study guided by Walker and Avant\u0026rsquo;s method\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e was conducted in seven stages. The first stage identified the central concept of low-cost simulation, supported by a previously published integrative review \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e across eight databases. The second stage specified the concept\u0026rsquo;s essential elements to distinguish LCS from related educational tools. The third stage articulated the theory\u0026rsquo;s purpose to explain the phenomenon and guide its application. The fourth stage identified dimensions and variables to establish the model\u0026rsquo;s conceptual architecture. The fifth stage identified antecedents and consequences to clarify conditions of use and practical impact. The sixth stage formulated the theory through propositions that linked the concept to related factors and aligned low-cost technology with high-stakes educational goals. The seventh stage evaluated the theory by assessing internal consistency and logical robustness to support subsequent empirical testing. This study did not involve human participants or data collection and, therefore, did not require institutional review board approval.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStages 1, 2, and 3: Identification, Definition, and Purpose of the Concept\u003c/h2\u003e \u003cp\u003eThe central concept is the use of low-cost simulators in clinical simulation (LCS-CS) within healthcare education. Their use has expanded, particularly in settings where financial and infrastructural constraints restrict access to commercial devices. However, the concept remains inconsistently defined across the literature, complicating comparative research, curriculum planning, and standardization of simulation interventions. Accordingly, systematic concept clarification was undertaken before theory development.\u003c/p\u003e \u003cp\u003eLCS are defined as \u0026ldquo;simulation devices used for clinical skills training and simulation-based education, developed or acquired at a lower cost than comparable commercial models, generally characterized by accessible materials, feasibility of assembly, and replicability, and may include different levels of technological complexity, depending on the intended educational purpose\u0026rdquo;.\u003c/p\u003e \u003cp\u003eThis concept analysis clarified the meaning and boundaries of LCS-CS, thereby supporting its operationalization within the proposed framework. A recurrent source of ambiguity was the interchangeable use of the terms low-cost and handmade. Here, low-cost refers to affordability and access feasibility relative to context and comparable commercial alternatives, whereas handmade refers to the production mode. This distinction matters because handmade devices may not remain low-cost over time when durability and replacement costs are considered.\u003c/p\u003e \u003cp\u003eAcross identified uses, LCS were described in relation to technological complexity, production mode, and relative cost, informing the framework\u0026rsquo;s classification approach. Concept clarification also informed the framework\u0026rsquo;s implementation attributes, defined in Table\u0026nbsp;1: affordability, access feasibility, sustainability capacity, manufacturability and assembly feasibility, portability, purpose-appropriate fidelity, enabling technology integration, replicability and consistency, versatility, and contextual adaptability.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;1.\u003c/b\u003e \u003cem\u003eLow-Cost simulator attributes and definitions\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStage 4: Identification of Dimensions and Variables\u003c/h3\u003e\n\u003cp\u003eMeasurable variables were grouped into four domains to enable empirical testing of pathways from context through implementation and modulators to outcomes.\u003c/p\u003e\n\u003ch3\u003eContextual variables (antecedents)\u003c/h3\u003e\n\u003cp\u003eExternal conditions that define the background conditions within which LCS may be considered a viable and desirable educational solution. These include: (ANT1) educational need, referring to demand for competency-based clinical training supported by safe, structured, and reproducible learning experiences; (ANT2) availability of commercial simulation models, describing the extent to which commercially produced and standardized simulation equipment is accessible to an institution; (ANT3) institutional infrastructure, referring to baseline physical and technical capacity (e.g., availability of simulation spaces, general equipment, and maintenance resources); (ANT4) equity-related access constraints, describing socioeconomic barriers affecting access to educational resources and participation in simulation-based education; (ANT5) educational policies, including regulatory and curricular requirements that govern teaching approaches and assessment expectations; and (ANT6) institutional commitment to sustainability, reflected in strategic priorities and procurement policies that focus on reuse, repairability, and environmentally responsible practices.\u003c/p\u003e\n\u003ch3\u003eProcess variables (implementation)\u003c/h3\u003e\n\u003cp\u003eImplementation refers to how LCS-CS is applied. This domain encompasses both the delivery of simulation-based learning experiences and the technical and functional characteristics of the simulators that support these activities. Accordingly, implementation is operationalized through a set of measurable simulator attributes (SA) with operational definitions provided in Supplemental Digital Content 1. These attributes enable consistent characterization and comparison of LCS across educational contexts and facilitate systematic evaluation of implementation.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModulating variables (modulators)\u003c/h2\u003e \u003cp\u003eInstructional and institutional conditions associated with variation in LCS-CS implementation quality and consistency. These include: (MOD1) instructional alignment and pedagogical strategy, encompassing accordance with educational objectives and the teaching approaches adopted (e.g., deliberate practice, feedback processes, debriefing structure, and integration within wider teaching plans); (MOD2) faculty competence and qualifications, describing educators\u0026rsquo; preparation and competence in simulation facilitation, debriefing, and assessment; (MOD3) learner readiness, reflecting learners\u0026rsquo; baseline experience level, prior exposure, as well as readiness for simulation-based activities; and (MOD4) institutional capacity for implementation, encompassing institutional culture of innovation and available technical and pedagogical support, including simulation-specific infrastructure, staff support, and organizational arrangements that enable ongoing delivery.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcome variables (consequences)\u003c/h3\u003e\n\u003cp\u003eExpected effects of LCS-CS within healthcare education. These include: (PO1) access to clinical simulation, reflected in expanded opportunities for participation in simulation-based learning; (PO2) learning and competence development, encompassing the development and consolidation of competencies and skills, including technical and non-technical performance; (PO3) instructional quality and teaching capacity, representing the quality of implementation (instructional design, facilitation, debriefing, and learner assessment) and the refinement of teaching practices enabled by low-cost simulation; and (PO4) efficiency and sustainability, including resource utilization and cost-related indicators (e.g., reduced reliance on consumable materials), reuse of materials, and the sustained viability of simulation programs supported by low-cost solutions. The distal outcome is healthcare quality (DO1), conceptualized as downstream improvement associated with sustained and effective LCS-CS over time.\u003c/p\u003e\n\u003ch3\u003eStage 5: Formulation of the Theoretical Proposition\u003c/h3\u003e\n\u003cp\u003eThe MRTF proposes that, under antecedent conditions including educational need (ANT1), limited availability of commercial simulation models (ANT2), constrained institutional infrastructure (ANT3), equity-related access constraints (ANT4), educational policies (ANT5), and institutional commitment to sustainability (ANT6), the use of low-cost simulators in clinical simulation (LCS-CS) becomes a feasible and context-responsive educational strategy. Implementation is operationalized through simulator attributes (SA1\u0026ndash;SA10) that determine feasibility, functionality, scalability, and fit-for-purpose. However, these attributes alone are insufficient to ensure consistent educational gains. The extent to which LCS-CS produces proximal outcomes, access to clinical simulation and practice-based learning (PO1), learning and competence development (PO2), instructional quality and teaching capacity (PO3), and efficiency and sustainability (PO4) depends on key modulators, including instructional alignment and pedagogical strategy (MOD1), faculty competence and qualifications (MOD2), learner readiness (MOD3), and institutional capacity for implementation (MOD4). Over time, sustained improvements in these proximal outcomes are expected to contribute to the distal outcome of improved quality of healthcare delivery (DO1).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStage 6: Theoretical Model\u003c/h2\u003e \u003cp\u003eStage 6 formalized the MRTF as a theoretical model by integrating the framework domains into a coherent explanatory structure. Figure\u0026nbsp;1 graphically presents the proposed MRTF model, depicting the hypothesized relationships among antecedents, simulator attributes, modulators, and outcomes, while Table\u0026nbsp;2 specifies these relationships through a set of testable propositions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1.\u003c/b\u003e \u003cem\u003eMiddle-range theoretical framework for the use of low-cost simulators in healthcare education: a graphic representation.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;2.\u003c/b\u003e \u003cem\u003eThematic axes and theoretical propositions of the middle-range theoretical framework for the use of low-cost simulators in healthcare education.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStage 7: Empirical Testing\u003c/h2\u003e \u003cp\u003eEmpirical testing of the framework involves systematically observing and measuring its constructs and propositions. Stage 7 was not conducted in the present study because the primary aim was theory construction and conceptual specification. Accordingly, empirical testing is positioned as the next step in the research program and will be addressed in future studies.\u003c/p\u003e \u003cp\u003eAs identified in the introduction and summarized in the Supplemental Digital Content 2, current empirical evidence in the low-cost simulation literature is concentrated on simulator attributes (SA) and feasibility, and, in some cases, extends to proximal outcomes, particularly learning and competence development (PO2) and instructional quality and teaching capacity (PO3). In contrast, there is substantially less evidence explicitly examining modulators as causal conditions, especially instructional alignment and pedagogical strategy (MOD1) and institutional capacity for implementation (MOD4), measured through formal constructs. Evidence regarding the distal outcome of improved quality of healthcare delivery (DO1) remains minimal, as expected given its longer causal trajectory and the methodological demands of longitudinal evaluation. This distribution of evidence provides a diagnostic snapshot of the field and motivates future empirical testing.\u003c/p\u003e \u003cp\u003eAs part of the empirical literature, the Instrument for the Evaluation of Low-Cost Simulators\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e was developed and content validated to assess the presence of simulator attributes aligned with this framework. The instrument includes 25 items across six factors that operationalize SA1\u0026ndash;SA10: \u0026ldquo;Cost\u0026rdquo; includes items related to affordability (SA1); \u0026ldquo;Accessibility\u0026rdquo; captures access feasibility (SA2) and portability (SA5); \u0026ldquo;Technology, manufacturing and reproducibility\u0026rdquo; captures manufacturability and assembly feasibility (SA4), enabling technology integration (SA7), replicability and consistency (SA8), and sustainability capacity (SA3); \u0026ldquo;Realism\u0026rdquo; reflects fidelity (SA6); \u0026ldquo;Versatility\u0026rdquo; reflects versatility (SA9) and contextual adaptability (SA10); and \u0026ldquo;Usability\u0026rdquo; captures deployment and handling indicators that overlap with SA2, SA4, SA5, and contextual adaptability (SA10).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe proposed MRTF advances theory-informed LCS-CS within healthcare education, particularly in resource-limited environments. By integrating simulator attributes, contextual conditions, implementation modulators, and proximal and distal outcomes within a single explanatory structure, the framework supports systematic planning and evaluation of LCS-CS and enables cumulative, theory-driven empirical testing.\u003c/p\u003e \u003cp\u003eThe MRTF is consistent with established simulation theories and standards while addressing a specific gap related to low-resource decision pathways. The Jeffries Simulation Theory\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e emphasizes instructional design, learning outcomes, and faculty preparation, but it does not explicitly represent the constraints, trade-offs, and feasibility decisions that shape simulation design and delivery when commercial resources and institutional infrastructure are limited. By specifying antecedent conditions, simulator attributes as implementation constructs, and modulators that shape implementation quality and the translation of LCS-CS into outcomes, the MRTF extends this foundation for contexts where sustainability and equity are central priorities.\u003c/p\u003e \u003cp\u003eA key contribution of the framework is its shift from device-centered explanations toward an implementation-focused account of effectiveness. Rather than treating LCS performance as a direct function of simulator design or technological sophistication, the MRTF specifies the conditions under which LCS-CS is likely to generate meaningful educational and organizational benefits and the mechanisms by which it is likely to do so. From this perspective, simulator attributes (SA1\u0026ndash;SA10) are necessary but not sufficient. Their educational value depends on the strength of modulators, particularly instructional alignment and pedagogical strategy (MOD1), faculty competence and qualifications (MOD2), learner readiness (MOD3), and institutional capacity for implementation (MOD4). This emphasis positions implementation quality and contextual fit as key explanatory drivers and reframes fidelity as purpose-appropriate rather than technology-determined. This emphasis also contributes to ongoing debates in simulation research, including the assumption that higher technological fidelity necessarily produces superior outcomes. The MRTF conceptualizes effectiveness as emerging from the alignment among simulator functionality, scenario design, and pedagogical strategy. Accordingly, low-technology solutions may yield comparable outcomes when learning objectives, instructional alignment, and facilitation practices are coherent and adequately supported.\u003c/p\u003e \u003cp\u003eThe MRTF also provides a practical planning logic for educators and institutions. For example, in a resource-limited healthcare program intending to strengthen intravenous cannulation, educators may first recognize persistent performance gaps in assessment and clinical placement (ANT1), restricted access to commercial devices (ANT2), and limited simulation space (ANT3). They respond by developing or selecting LCS that can be deployed at scale, prioritizing affordability (SA1), access feasibility (SA2), manufacturability and assembly feasibility (SA4), replicability and consistency (SA8), and purpose-appropriate fidelity (SA6) aligned with learning objectives. To ensure that these attributes translate into learning, sessions are embedded within a structured teaching plan with well-defined objectives and brief, standardized debriefing (MOD1), and faculty are prepared to provide feedback and facilitate practice using consistent criteria (MOD2). Learners complete short preparatory activities before hands-on sessions and progress through staged practice to ensure readiness (MOD3), while the institution provides protected timetables, basic technical support, and a simple system for storage, distribution, and replacement of components (MOD4). Implementation is then tracked through increased participation and practice opportunities (PO1), improved technical and non-technical performance in skills assessments (PO2), more consistent facilitation and debriefing quality across groups (PO3), and reduced costs per learner alongside more efficient reuse and longer-term feasibility (PO4). Consistent improvements over successive cohorts provide a plausible pathway to downstream gains in healthcare quality (DO1).\u003c/p\u003e \u003cp\u003eConformity with the Healthcare Simulation Standards of Best Practice\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e further supports the framework\u0026rsquo;s relevance for implementation. The modulators domain is consistent with standards emphasizing facilitation, debriefing, and outcomes-driven design, reinforcing that learning quality depends on structured preparation and reflection processes rather than technological sophistication alone. The institutional capacity construct (MOD4) also concurs with operational standards that require planning, staffing, and governance to support safe and sustainable delivery, including in low-cost contexts. Finally, the outcomes domain emphasizes measurable educational and organizational effects (PO1\u0026ndash;PO4) while situating improved quality of healthcare delivery (DO1) as a longer-term outcome requiring longitudinal research designs.\u003c/p\u003e \u003cp\u003eThe framework also makes explicit why equity and sustainability should be treated as implementation-relevant constructs rather than aspirational goals. By operationalizing affordability, access feasibility, and sustainability capacity alongside contextual constraints, the MRTF supports decision-making that responds to resource limitations while preserving educational quality. This positioning supports curriculum and institutional planning to guarantee equitable access to simulation-based learning opportunities and sustainable delivery models.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the concept analysis and literature synthesis reflect the current state of the field, which shows heterogeneity in reporting and outcome measurement. As a result, some constructs, particularly modulators, are less consistently described in the empirical literature, limiting the extent to which they could be anchored to standardized indicators at this stage. Second, although the framework specifies a distal outcome, evidence linking LCS-CS to healthcare delivery outcomes is scarce and methodologically demanding. Improved quality of healthcare delivery should therefore be interpreted as a long-trajectory target mediated by sustained proximal improvements rather than an immediate effect. Third, the MRTF was developed through theory construction and conceptual specification, so Stage 7 empirical testing was not conducted. Although the framework is operationalized through measurable constructs and propositions, its explanatory pathways require prospective evaluation throughout various contexts. Finally, the model is intended to be broadly suitable across healthcare education settings, yet variation in curricular structures, regulatory requirements, and institutional infrastructure may affect implementation pathways, pointing to the need for multisite testing and refinement.\u003c/p\u003e \u003cp\u003eFurther investigations should prioritize empirical testing using designs that systematically observe and measure the framework constructs and propositions across diverse educational contexts. Current low-cost simulation research is concentrated on simulator attributes and feasibility and only partially addresses proximal outcomes, whereas modulators, particularly instructional alignment and pedagogical strategy (MOD1) and institutional capacity for implementation (MOD4), are less often operationalized as causal conditions; evidence for DO1 also remains limited given its distal and longitudinal nature. As an initial step, pilot and feasibility studies should operationalize antecedent conditions, simulator attributes, and modulators as measurable indicators using standardized measures, with particular emphasis on MOD1 and MOD4. Subsequent multisite studies should test key pathways linking implementation to proximal outcomes using comparable metrics and mixed method approaches that capture both educational effectiveness and implementation quality. Prospective designs are also needed to examine distal outcomes over longer causal trajectories. Collectively, this roadmap supports cumulative, theory-driven evidence generation to refine the MRTF and identify the most testable propositions across contexts.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study developed an MRTF that clarifies how LCS-CS can expand simulation-based education in healthcare while supporting sustainability and equity goals. By specifying contextual antecedents, simulator attributes, modulators of implementation quality, and proximal and distal outcomes, the framework provides an operational basis for systematic planning, evaluation, and cumulative research.\u003c/p\u003e \u003cp\u003eThe framework conceptualizes LCS not as a purely technical or economic solution but as an implementation strategy whose value depends on the interaction between simulator characteristics and pedagogical and institutional conditions. Consistent educational gains are more likely when purpose-appropriate fidelity and implementation rigor are supported through instructional alignment and pedagogical strategy, faculty competence, learner readiness, and institutional capacity.\u003c/p\u003e \u003cp\u003eBy operationalizing simulator attributes as measurable constructs, the MRTF supports decisions that respond to resource constraints while preserving educational quality and provides a theory-informed basis for curriculum design, institutional strategy, and policy discussions directed at equitable and sustainable expansion of simulation-based learning opportunities.\u003c/p\u003e \u003cp\u003eFinally, by providing shared conceptual language and explicit propositions, the MRTF supports a shift from isolated feasibility demonstrations to systematic, theory-driven testing and implementation. Over time, this approach can strengthen the evidence base needed to evaluate pathways from proximal educational and organizational outcomes to distal improvements in healthcare delivery.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eHuman Ethics and Consent to Participate:\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003eConflicts of interest/Competing interests:\u0026nbsp;The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eAvailability of data and material:\u0026nbsp;The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eFunding:\u0026nbsp;Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDeclarations, Ethics approval, and consent to participate:\u0026nbsp;Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication:\u0026nbsp;Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions: All authors reviewed and approved the final version submitted for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShin S, Park JH, Kim JH. 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Clin Simul Nurs. 2021;58:1\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ecns.2021.08.006\u003c/span\u003e\u003cspan address=\"10.1016/j.ecns.2021.08.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Simulation Training, Education, Nursing, Clinical Competence, Models, Educational, Sustainable Development","lastPublishedDoi":"10.21203/rs.3.rs-9364818/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9364818/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eSimulation-based education is widely used to build clinical competence in healthcare education. Low-cost simulators are increasingly adopted, especially in resource-limited and sustainability-focused settings. However, limited theoretical frameworks restrict systematic evaluation and broad implementation. This study aimed to develop a middle-range theoretical framework explaining the use of low-cost simulators in clinical simulation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWalker and Avant\u0026rsquo;s theory construction method was used to define core domains, measurable constructs, and testable propositions, supported by previous concept analysis and literature synthesis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe framework comprises four domains: Antecedents (contextual variables describing the conditions that justify and enable use of low-cost simulators, such as educational need, institutional infrastructure, and equity-related access constraints); Implementation (process variables describing how low-cost simulation is applied, operationalized through simulator attributes including affordability, access feasibility, and contextual adaptability); Modulators (modulating variables influencing implementation quality and consistency, such as instructional alignment and pedagogical strategy, faculty competence and qualifications, and learner readiness); and Consequences (outcome variables), including proximal outcomes (access to clinical simulation and practice-based learning, learning and competence development, efficiency and sustainability) and a distal outcome (improved quality of healthcare delivery). Thirteen propositions specifying relationships among these domains were formulated to support future empirical testing.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe proposed framework provides an operational guide to evaluate, implement, and scale low-cost simulation in nursing and other health professions education.\u003c/p\u003e","manuscriptTitle":"Development of a Middle-Range Theoretical Framework for Low- Cost Simulators in Clinical Simulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 17:40:22","doi":"10.21203/rs.3.rs-9364818/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec5bac82-8c53-413f-88d0-e237da9ef568","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-13T08:06:06+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"34260920444107433814746270767691661108","date":"2026-05-11T13:53:13+00:00","index":51,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T04:11:24+00:00","index":41,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T08:14:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 17:40:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9364818","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9364818","identity":"rs-9364818","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}