
==== Front
BMJ Open
BMJ Open
bmjopen
bmjopen
BMJ Open
2044-6055
BMJ Publishing Group BMA House, Tavistock Square, London, WC1H 9JR

39289017
10.1136/bmjopen-2024-084622
bmjopen-2024-084622
Protocol
Medical Education and Training
1709
1506
Virtual clinical simulation as a paedagogical strategy in healthcare learning: Evidence and Gap Map protocol
http://orcid.org/0009-0005-4426-1442
Engler Jamille Costa 1jamille.engler@grad.ufsc.br

http://orcid.org/0000-0003-2988-2230
Castro Luiza Sheyla Evenni Porfírio Will 12lsepwill@gmail.com

http://orcid.org/0000-0002-7988-8756
Guesser Joice Cristina 12joiceguesser@gmail.com

http://orcid.org/0000-0002-5810-1774
da Silva Flôr Janaína 12janainasflor@gmail.com

http://orcid.org/0000-0002-7986-8317
De Luca Canto Graziela 3graziela.canto@ufsc.br

http://orcid.org/0000-0002-0511-8994
Zimmermann Glaucia Santos 3glaucia.sz@ufsc.br

https://twitter.com/monicalinoo
http://orcid.org/0000-0003-0828-7969
Lino Monica Motta 124monica.lino@ufsc.br

1 Department of Nursing, Federal University of Santa Catarina, Florianopolis, Brazil
2 Postgraduate Program in Nursing, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil
3 Department of Dentistry, University of Santa Catarina, Florianópolis, Brazil
4 Postgraduate Program in Health Informatics, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil
Monica MottaLino; monica.lino@ufsc.br
None declared.

2024
17 9 2024
14 9 e08462224 1 2024
29 8 2024
Copyright © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

ABSTRACT

Introduction

Virtual clinical simulation involves creating and applying scenarios using technology like computers or virtual reality. This method provides a secure experiential learning environment, encouraging active student participation and stimulating clinical, critical and reflective thinking. This article outlines the development of the Evidence and Gap Map, which aims to identify, quantify and visually and interactively classify existing systematic reviews on the effectiveness of virtual clinical simulations in health professional training.

Methods and analysis

The Evidence and Gap Map will adhere to the Campbell Collaboration Guidelines. Bibliographic searches in six databases will follow inclusion criteria determined by the Population, Intervention, Comparison, Outcome and Study design strategy. After the initial calibration, two reviewers will independently apply the inclusion and exclusion criteria to the title and abstract of each identified study, with subsequent full reading of the selected articles. The methodological quality of the included systematic reviews will be assessed with the AMSTAR 2 tool. The map will be developed using the EPPI-Mapper software.

Ethics and dissemination

There is no requirement for ethical approval for this systematic review. On completion, it will be published in a peer-reviewed academic journal and presented at a conference. This review protocol was registered on the Open Science Framework platform (OSF Associated Project Registration: osf.io/r6wdc and received the following DOI: 10.17605/OSF.IO/R6WDC).

Health Education
eHealth
Systematic Review
Health Workforce
http://dx.doi.org/10.13039/501100003593 Conselho Nacional de Desenvolvimento Científico e Tecnológico 308443/2020-9
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pmcSTRENGTHS AND LIMITATIONS OF THIS STUDY

A significant strength of this systematic review is the methodology that provides a structured and systematic approach.

The methodological quality assessed using the AMSTAR 2 tool is a key strength of this systematic review.

Some limitations include heterogeneity of studies with diverse methodologies, publication bias, quality and contextual variations.

Introduction

Clinical simulations are a teaching method based on the creation of simulated clinical scenarios that imitate real-life situations from professional healthcare practice, in a controlled and safe environment.1 2 This pedagogical method has become increasingly common in the training of health professionals, as it offers an experiential learning environment where the student can make mistakes and successes, without putting the integrity of real patients at risk.3 4 This teaching tool provides an environment where the student can consolidate their knowledge acquired in theory and have an active participation in their own learning, stimulating clinical, critical and reflective thinking. There are several types of clinical simulation that can be used in teaching, one of which is virtual simulation.57

Virtual clinical simulation consists of the development and application of simulation scenarios using technological resources, such as computers or virtual reality (VR).5 The increasingly frequent use of technological tools is a global trend, especially among the younger population, which in part has already grown up in this digital environment.8 Digital resources have shown an increase in their integrality with learning spaces, with several possibilities for their inclusion in this environment.9 Technology allows distance learning, which facilitates the access and dissemination of knowledge.10 Clinical simulations, combined with the virtual environment, provide new possibilities and teaching methods in the health area.81113

Evidence and Gap Map (EGM) represents a systematic synthesis of evidence, providing a visual display of the pertinent information related to a specific research question. This map adheres to the established principles of systematic reviews (SR), encompassing the definition of a PICOS (Population, Intervention, Comparison, Outcome and Study design) framework, a thorough and inclusive search process, meticulous screening against explicit inclusion and exclusion criteria and the systematic application of coding, analysis and reporting methodologies.14

In this context, the development of an EGM aims to map existing evidence on the effectiveness of using virtual clinical simulations in the training of health professionals. Through it, it will be possible to identify gaps in knowledge, where there is a lack of studies or evidence, and it may be recommended to carry out research focused on these areas.

This study aims (1) to identify and quantify existing SR on the effectiveness of using virtual clinical simulations as a learning strategy in the training of health professionals; (2) to map, through the EGM, the existing SR in the area in a visual and interactive way and (3) to identify knowledge gaps present in this area of research, through evidence mapping.

Within this context, the question this research seeks to answer is: ‘What is the volume and diversity of SR on the use of virtual clinical simulation as a learning strategy in the training of health professionals?’

Methods and analysis

Study design

This study will follow the Campbell Collaboration Guideline.14 The protocol was registered on Open Science Framework-OSF (OSF Associated Project Registration: osf.io/r6wdc) and received the following DOI: 10.17605/OSF.IO/R6WDC.

Eligibility criteria

The inclusion criteria were defined using the PICOS strategy to ensure the coherence of the review. The PICOS strategy for this work will be as follows:

P: health professionals in training

I: use of virtual clinical simulations in education

C: traditional teaching methods

O: effectiveness of using virtual clinical simulations in the learning and training of health professionals. To achieve this, the Primary Outcomes (Improvement in Practical Skills; Competence and Enhancement in Decision-Making; Effective Application of Theoretical Knowledge to Simulated Practice; Student Satisfaction) and Secondary Outcomes (Interprofessional Collaboration; Student Self-Efficacy with Confidence Development; Transfer to Real Clinical Practice with Successful Application; Active Engagement with Effective Participation and Involvement; Effectiveness with the overall assessment of the Programme’s effectiveness) will be utilised.

S: systematic review.

This will allow for an efficient search for existing evidence and identification of knowledge gaps for future research (figure 1).

Figure 1 The inclusion criteria defined through the PICOS strategy. Florianópolis, SC, Brazil. 2024. PICOS, Population, Intervention, Comparison, Outcome and Study design.

Studies that adhere to the criteria proposed by the Cochrane Handbook for Systematic Reviews of Interventions will be considered as a SR.15 The criteria include the following:

Have a clearly stated set of objectives with predefined eligibility criteria for studies.

Have conducted a systematic search that attempted to identify all studies that met the stated eligibility criteria.

Have employed an explicit, reproducible methodology.

Have assessed the validity of the findings of the included studies through the risk of bias assessment, for example.

Have systematically presented and synthesised the characteristics and findings of the included studies.

Information sources

The search will be carried out in the following databases: Cumulative Index to Nursing and Allied Health Literature—CINAHL (via EBSCO), Latin American and Caribbean Health Science Literature—LILACS (via Virtual Health Science Library—VHS), Medical Literature Analysis and Retrieval System Online—MEDLINE (via PubMed), Scientific Electronic Library Online—SciELO, Scopus (Elsevier) and Web of Science (via Clarivate Analytics).

A search will be carried out in the grey literature on the web through Google Scholar and in the ProQuest Dissertations and Theses Global database (via ProQuest). Manual reference checking and advanced citation searching will also be conducted on studies that meet our inclusion criteria.

Search strategy

Search strategies have been developed with the assistance of an experienced health sciences librarian and applied through a search on 20 January 2024. Furthermore, all steps prior to selection have already performed and everything from selection forward is forthcoming to start in August, 2024. The article selection is scheduled to occur between August and September 2024, with data extraction planned for October and November 2024, and data analysis set and EGM elaboration for December 2024. Accordingly, the study is designed to begin in August and conclude in December.

The search strategy combined search terms for SR in the use of virtual clinical simulation and evidence synthesis, utilising both controlled vocabulary when available (eg, MeSH) and free-text search. Search terms will be partially derived from the titles and abstracts of preidentified SR on the use of virtual clinical simulation and the search strategies of preidentified SR, as well as any relevant search filters. There will be no language or date restrictions.

The general concepts employed in the bibliographic search were virtual clinical simulations, education and health professionals. Within these groups, a series of terms were selected in order to cover the largest number of SR within the question of this research and to be able to more faithfully map the extent of existing studies. The bibliographic research in the selected databases will be carried out using the set of terms described below, in English, Portuguese and Spanish (figure 2). The English terms were employed across all databases, while the terms in Portuguese and Spanish will be utilised in the Google Scholar, LILACS and SciELO.

Figure 2 Terms in English, Portuguese and Spanish used in the bibliographic search. Florianópolis, SC, Brazil. 2024. VR, virtual reality.

The research was carried out on 20 January 2024, through all predefined databases. Due to this being a search for SR articles, the Review or SR filter was used when available. The number of results and the search terms applied to each database are described below (figure 3).

Figure 3 Search strategy used in selected databases e grey literature. VR, virtual reality.

Selection process

The files with the references from each database will be exported to EndNote Web and duplicate studies will be removed. In sequence, two reviewers (JCE and LSEPWC) will independently apply the inclusion and exclusion criteria to a representative sample of citations (eg, n=number of eligible studies). Decisions will be discussed in a group meeting to ensure consistent application of the criteria. This will allow us to clarify the inclusion and exclusion criteria, revising them when necessary, allowing consistent interpretation and judgement by reviewers (JCE and LSEPWC).

After completion of the initial calibration exercise, two reviewers (J.CE and LSEPWC) will independently apply the inclusion and exclusion criteria to the title and abstract of each identified study (phase 1).16 Disagreements will be resolved through discussion with a third reviewer.

Subsequently, we will obtain the full text of the articles selected in phase 1, again two reviewers (JCE and LSEPWC) will independently evaluate the full text of each record for inclusion in the final sample (phase 2), with disagreements resolved through discussion with a third reviewer (JCG). This will include deciding whether each study is a SR according to the criteria detailed above. The selection process will be meticulously outlined, presenting a comprehensive overview of the findings. The reasons for excluding each retrieved record in full text will be explicitly reported in an appendix.

Data collection process

The review team, comprising three researchers, will build a table with the data to be extracted from the final sample of studies. Once the selection of studies has been completed, the data extraction table will be tested by the review team on a sample of included studies. A reviewer (JCE) will perform data extraction. The data will be checked by a second reviewer (LSEPWC), with disagreements being resolved through discussion and, if necessary, involvement of a third reviewer (JCG) (figure 4).

Figure 4 Data extraction tool developed by the reviewers. Florianópolis, SC, Brazil. 2024.

Synthesis methods

For the development of the Evidence and Gap Map, the EPPI-Mapper software will be used.17 The graphical representation of the data collected will be through an interactive map, where the existing studies, separated by types of interventions and results and the quality of these studies will be easily observed. The size and configuration of the EGM will depend on the data collected during the survey.

The map will be divided into lines, which represent the domains of interventions found (eg, Interactive Clinical Case Scenarios, Virtual Patient Environments, Simulations of Clinical Procedures, Emergency Medical Simulations, Consultation Simulations, Pharmacology Simulations, VR and/or Augmented Reality, Specific Training Modules) and columns, which represent the primary and secondary outcomes, previous cited. These domains can be expanded on the map, as they are a larger grouping of several specific subgroups, making the search and presentation of data more precise. The correlation between a given intervention and result will be demonstrated in the cells where these parameters intersect, being represented by ‘bubbles’ that change size and colour depending on the quantity and quality of existing evidence. The quantity of studies will be directly represented by the increase in bubble size, while the quality will follow a specified colour pattern.

The user will be able to search for specific evidence of a particular intervention, outcome or correlation between the two and, by clicking on the corresponding bubble(s), they will be able to access the information from the studies that fit the definition. This information will be collected in the data extraction process and includes: DOI, review quality, year of publication; research questions; present population; intervention(s) present; results of interest and number of studies included.

Through this graphical representation model in the EGM (figure 5), the search for specific or general evidence within the topic of virtual clinical simulations will be interactive and easy to view and access. Knowledge gaps in this area will be easily identified by the empty spaces made up of cells that do not have studies for certain interventions/results or by cells that have low quantity or quality of studies. Understanding the general scenario of scientific development in the area of virtual clinical simulations will be easily visualised, and access to specific studies will be easy for users to obtain.

Figure 5 Evidence and Gap Map schematic representation. Florianópolis, SC, Brazil. 2024.

Methodological quality assessment

The type of analysis to be conducted involves the Analysis of Study Quality and the Relationship with Evidence Gaps, for this purpose, the quality assessment of the selected SR will be carried out using the AMSTAR 2 tool, which is an instrument specialised in the critical assessment of SR that include randomised and/or non-randomised studies of health interventions.18 The quality of the reviews will be classified according to the level of confidence in the study results, segmented into ‘high’, ‘moderate’, ‘low’ and ‘critically low’ categories, based on the criteria established through 16 questions.

Patient and public involvement

None.

Ethics and dissemination

There is no requirement for ethical approval for this SR. On completion, it will be published in a peer-reviewed academic journal and presented at a conference.

Data statement

This review protocol was registered on the Open Science Framework platform (OSF Associated Project Registration: osf.io/r6wdc and received the following DOI: 10.17605/OSF.IO/R6WDC).

Acknowledgements

The authors like to acknowledge the members of the Interdisciplinary Laboratory of Educational Technologies in Health (LITES/UFSC) for suggestions and outstanding scientific support.

Review Process File
17 09 2024

Funding: This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil; MML is a Productivity in Technological Development and Innovative ExtensionThis work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil; MML is a Productivity in Technological Development and Innovative Extension

Prepublication history for this paper is available online. To view these files, please visit the journal online (https://doi.org/10.1136/bmjopen-2024-084622).

Patient consent for publication: Not applicable.

Ethics approval: Not applicable.

Provenance and peer review: Not commissioned; externally peer reviewed.

Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting or dissemination plans of this research.
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