Introduction

The field of health professions education (HPE) recognizes that all healthcare providers should be competent and have access to optimal training resources to develop and maintain competencies [1]. Healthcare providers in rural and remote (R&R) areas of Canada, more specifically physicians and nurses, may not have the same access to skills development and maintenance opportunities as those in urban areas due to factors such as distance from urban centers and cost [2]. The inexperience and lack of training for healthcare providers is one factor that has impacted the quality of care received by Canadians in R&R settings [3]. One example is a lack of training on technical skills used to perform high-acuity low-occurrence (HALO) procedures. HALO procedures are rarely performed clinical procedures that are required to be performed urgently when needed [3]. Having limited training on how to perform HALO procedures can affect the health outcomes of a patient in emergency situations and in locations where accessing addition health care services is difficult. The College of Family Physicians of Canada (CFPC) highlights the need to develop contextual competencies of providers to practice in R&R clinical settings and recognizes the need to reform HPE due to the significance the level of education, in relation to training and competencies, has on adequately serving the health needs of the population [3]. To improve rural education programs, one of the objectives that the CFPC has identified is to develop tools to increase access to clinical training, with one such tool being simulation technology [3].

Simulation-based education (SBE) is defined as a replication of a real task (or patient encounter) for the purpose of training or assessment of quality improvement and is proven to be a key aspect of HPE [4]. SBE serves as an optimal technique to allow healthcare providers to develop and maintain skills and can be used for assessment and evaluation without endangering the safety of a patient [5]. The use of additive manufacturing allows for the improved availability of affordable simulators through a flexible, efficient, and sustainable production process that maintains high quality and low cost [6]. The most known form of additive manufacturing is three-dimensional (3D) printing. 3D printing technologies are a useful tool in medical training and healthcare due to its customizability to fit any context and the reduction of costs in production [7]. However, the development of simulators using additive manufacturing and 3D printing technologies is currently limited mainly to university or hospital-based research laboratories in urban areas of developed countries, and are rarely diffused to R&R settings, where they can create a greater impact [2, 8, 9]. Siraj et al. and Barth et al. report on the positive outcomes of using SBE and 3D printing technologies to train students and healthcare providers using simulators to learn and improve hands-on clinical skills [9, 10]. Developing simulators in a research laboratory using 3D printing technologies can help reduce costs and address gaps in HPE for R&R healthcare providers [10].

An example of a simulator developed in a university research laboratory include a 3D printed/silicone simulator to train on the intraosseous (IO) access skill, where a hole is drilled into the proximal tibia bone to deliver fluids and medication into the bone marrow [11]. The material cost to manufacture a simple IO simulator is $12.66 CAD and costs $53.89 CAD to manufacture an advanced IO simulator. When compared to a commercially available IO similar that retails for $414 USD, the simulator developed in the laboratory is produced at a fraction of the cost [11]. Another example is the development of a cost-effective cricothyroidotomy simulator for emergency medicine simulation training. Cricothyroidotomy is a procedure that allows for tracheal intubation in life-threatening situations [12]. The material cost of developing this anatomically accurate simulator using 3D printing technology is $3.63 CAD [12]. The cost of developing the simulator is covered through research and development funding whereby the research laboratory designs, prototypes and conducts research in the areas of validity, acceptability, feasibility and efficacy/effectiveness [11, 12]. Once the research and development process has concluded, the healthcare system requires these simulators to be manufactured to address their training needs, however, there is no mechanism or model to facilitate this process.

Public health systems research (PHSR) is a growing focus in Canada that looks at financing, delivery, and impact of public health services [13]. One of the top six priorities of PHSR in Ontario is partnerships and linkages. This priority highlights the importance of partnerships between various sectors (i.e. healthcare providers, educational institutions, community-based organization, government, etc.) to improve the performance of the public health system [13]. Establishing partnerships between different sectors, such as university research laboratories, for-profit organizations (FPOs), and not-for-profit organizations (NPOs), can improve capacity building of healthcare providers and knowledge exchange within that healthcare system. To date, there are no specific SBE-focused partnership models that address how 3D printed simulation technology can make its way from university research laboratories into the HPE system, especially in the R&R context. Establishing a partnership between relevant organizations can facilitate the process of delivering simulators that are developed in the lab to hospitals and educational institutions in R&R parts of Canada to train healthcare providers. A multi-institutional partnership can help overcome logistical challenges experienced by one sector, such as manufacturing the simulator, by pooling resources to provide simulators to R&R healthcare providers in a way that is beneficial to all partners [1, 14]. Making SBE available to communities in need can improve health outcomes through its link to healthcare provider training [15]. Therefore, the purpose of this scoping review is to understand the existing literature and identify if a model exists that outlines how a university research laboratory can collaborate with a FPO and NPO.

Protocol design

The methodological framework introduced by Arksey and O’Malley [16] will be used to guide this scoping review by adhering to the five stages of the framework: 1) identifying the research question, 2) identifying relevant studies, 3) study selection, 4) charting the data, and 5) collating, summarizing and reporting the results. The scoping review will be conducted and reported in adherence with the Joanna Briggs Institute Manual for Evidence Synthesis’ scoping review chapter [17], and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist [18]. This study does not require ethics approval as the scoping review methodology involves reviewing and collecting data from publicly available materials.

Dissemination

Results of this scoping review will provide an understanding on the extent of existing literature on diffusing simulators to R&R areas for healthcare provider training through a multi-institutional partnership. Additionally, the results will help identify the gaps in knowledge to establishing and sustaining a partnership that has a SBE focus. The results of this scoping review will be relevant and informative for various stakeholders: academic bodies, researchers, NPOs, simulation technologists, and healthcare providers, as a partnership addressing the identified research gap can be mutually beneficial to all stakeholders involved. In relation to dissemination, findings from this scoping review will be submitted for publication to a peer-reviewed journal.

Strengths and limitations of the study

• This scoping review will be the first to examine literature to identify a partnership model that focuses on implementing SBE practices, especially in the Canadian R&R context.
• The identification of articles will be done through five published literature databases, three grey literature databases, and snowball references.
• Based on preliminary searches, there is no model that specifically addresses the research question. Therefore, comparable models will be chosen that can provide information for the creation of a new model specific to the research question.

Supporting information