Participate in Research

Nurse Educators are invited to participate in a Delphi Study that aims to understand virtual simulation debriefing practices, identify gaps in current best practice guidelines and establish best practice guidelines for debriefing virtual simulations. Nurse educators with experience in creation, implementation and/or evaluation of simulations, virtual simulations and their debriefs are eligible to participate in this study.

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A Realist Approach to Understanding How Nursing Students Learn to Provide Equitable Person-centred Care in Simulation

Dear Simulation Colleague,

I hope this message finds you well. I am writing to invite you to contribute as a key informant in my research study titled A Realist Approach to Understanding How Nursing Students Learn to Provide Equitable Person-centred Care in Simulation.

This study examines how and why simulation-based education supports nursing students’ learning of equitable, person-centred care. Using a realist methodology, the project aims to identify the contextual conditions and underlying mechanisms that enable or constrain learning, and to generate theory-informed, practical guidance for simulation design and facilitation in nursing education.

Given your recognized expertise in simulation-based nursing education, your perspectives would be highly valuable to this work. I am seeking input from experienced educators and scholars to help refine ideas and theory about how and why simulation engenders learning, inform the development of a simulation intervention, and interpret emerging findings. Your contributions will help ensure that the study is grounded in current scholarship and best practices.

If you agree, your involvement would include participating in approximately four 60-minute focus groups over an 18-month period. These sessions will be scheduled at times convenient to participants and may take place online via Microsoft Teams. Discussions will focus on your professional perspectives regarding how simulation facilitates learning, how person-centred care and equity can be supported pedagogically, and how theory can inform simulation design and evaluation.

Participation is optional, and there are minimal risks associated with involvement. All information will be kept confidential, and identifying details will not be included in any reports or publications. A detailed consent form outlining the study procedures and protections will be provided prior to participation.

Thank you for considering this invitation. I greatly value your time and expertise and would be honoured by your contribution to this work.

If you would like to participate, have any questions or would like more information about the study, please contact Andrea Orr at Andrea.Orr@twu.ca

Warm regards,

Andrea Orr, RN, PhD(c)

Principal Investigator

Assistant Professor, School of Nursing

22500 University Dr., Langley, BC V2Y 1Y1

T. 604.513.2121 x 3288 Email: andrea.orr@twu.ca

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Examining how AI-assisted qualitative evidence synthesis compares with human-led synthesis

Dear CAN-Sim Members

I am developing a methodological research study examining how AI-assisted qualitative evidence synthesis compares with human-led synthesis. Rather than re-running literature searches, the study will re-analyze the evidence synthesis phase of previously completed scoping reviews using contemporary AI tools, while keeping the underlying evidence identical. The goal is to understand where AI reproduces, extends, or diverges from the original human analysis, and to identify the aspects of qualitative evidence synthesis that continue to require human interpretation.

To conduct this work, I am looking for approximately six completed scoping reviews that meet the following criteria:

• Published before January 2023 (to ensure the review was completed before the widespread adoption of generative AI).

• Published in a journal with an impact factor of at least 2.

• Conducted without the use of AI during the review process.

• Led by independent research teams (i.e., only one review per research group).

• Authors are willing to share the review materials required for the re-analysis.

The materials requested would ideally include:

• the final data extraction table(s);

• the PDFs of included studies;

• coding frameworks or analytic notes, if available; and

• the published manuscript.

Importantly, the original review will not be repeated. The literature search, study selection, and data extraction remain unchanged. The study focuses exclusively on re-analyzing the existing extracted data using AI and comparing the resulting synthesis with the original human-led synthesis.

Examples of suitable reviews include topics such as simulation debriefing, virtual simulation, standardized patients, faculty development, simulation assessment, psychological safety, interprofessional simulation, simulation technologies, or any other simulation-related scoping review.

My team will conduct all of the AI analyses, comparisons, and manuscript preparation. Collaborating authors would contribute by providing the original review materials, interpreting the findings, and reviewing the manuscript. Lead authors of included reviews would be invited to join the study team as co-authors.

I believe this study has the potential to generate important evidence regarding the role of AI in evidence synthesis while highlighting where human expertise remains essential.

Thank you very much for considering this request.

Amina Silva, BsN, RN, MsN, PhD

Pronouns: she/her/hers

Assistant Professor, Department of Nursing

Brock University | Faculty of Applied Health Sciences

Niagara Region | 1812 Sir Isaac Brock Way | St. Catharines, ON L2S 3A1

T 905 688 5550 ext.4282

Email: asilva@brocku.ca