A1 Emerging Insights into How Previous Experience and Perceived Realism Influence Performance in a Multi-Patient Simulation: A Mixed-Methods Study with Senior Undergraduate Nursing Students
Bibliographic record
Abstract
Introduction: A multi-patient simulation involving patients with acute health challenges was co-created by nursing faculty at the University of New Brunswick, Canada. The integration of this simulation occurred during the 2023 Fall term. Presented findings are focussed on data collected in the 2024 Fall term as research leads obtained ethical approval prior to this second offering. Although simulation-based experiences (SBEs) are well established as effective tools in building capacity in health care programs [1], the use of multi-patient simulations in support of skills such as clinical judgement and time management remain underexplored. The National Council State Boards of Nursing’s Clinical Judgement Measurement Model (CJMM)[2] helped frame learning objectives while INASCL standards were adhered to in the design of this simulation [3]. The purpose of this presentation is to share key findings and recommendations for a study exploring student perceptions of this multi-patient SBE. Methods: A mixed-methods approach was used in this study. Quantitative data were collected using pre- (n=70) and post-(n=60) simulation quizzes, with questions aligned to learning objectives. These quizzes assessed students’ knowledge and clinical judgement before and after the simulation. Qualitative data were collected through two focus groups (n=7) which included an exploration of students’ perceptions of elements impacting their ability to meet learning objectives. Quantitative data were analysed using descriptive statistics. Content analysis was used to identify key concepts which were organized into categories. Results: Quiz responses between subgroups of students were compared – students were either enrolled in the BN program through a bridging model or entered through a four-year pathway. All students scored poorly on questions involving teamwork and scope of practice considerations. In contrast, students who entered the BN program through the bridging model scored significantly higher on time management. Content analysis of focus group data revealed key categories: 1) knowing what to expect and what is expected of me; 2) realism as a performance factor; and; 3) acknowledging the impact of past experience. Discussion: Findings from this study offer insights into how senior nursing students experience and respond to a multi-patient simulation. Relationships between previous clinical experience, preparation, perceived realism, and the link to performance have implications for simulation design and teaching and learning strategies beyond a simulation context. A limitation of this study is the focus group participants included only students enrolled in the four-year pathway. Ethics Statement: As the submitting author, I can confirm that all relevant ethical standards of research and dissemination have been met. Additionally, I can confirm that the necessary ethical approval has been obtained, where applicable. References 1. Bray L, Østergaard D. A qualitative study of the value of simulation-based training for nursing students in primary care. BMC Nursing. 2024;23(1):1–10. doi: 10.1186/s12912-024-01886-0. 2. National Council of State Boards of Nursing. (2019). Clinical judgment measurement model. Next Generation NCLEX News,13, 1–6. Available from: https://www.ncsbn.org/public-files/NGN_Winter19.pdf 3. INACSL Standards Committee, Watts PI, McDermott DS, Alinier G, Charnetski M, Ludlow J, Horsley E, Meakim C, Nawathe P. Healthcare Simulation Standards of Best Practice® Simulation Design. Clinical Simulation in Nursing, 2021;58:14–21. doi: 10.1016/j.ecns.2021.08.009.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".