EXPLORING SIMULATION UTILIZATION AND SIMULATION EVALUATION PRACTICES AND APPROACHES IN UNDERGRADUATE NURSING EDUCATION
Bibliographic record
Abstract
Simulation-based learning (SBL) is rapidly becoming one of the most significant teaching-learning-evaluation strategies available in undergraduate nursing education.While there is indication within the literature and anecdotally about the benefits of simulation, abundant and strong evidence that supports the effectiveness of simulation for learning and evaluation in nursing education is slow to emerge and has yet to be fully established.As the use of SBL increases in nursing education, the need to evaluate students appropriately, accurately, and in reliable ways intensifies.Furthermore, as nursing programs increasingly consider SBL as direct clinical replacement in the context of increased student enrolment and dwindling clinical placements, standardized evaluation must play a vital role.Our study investigated simulation utilization and simulation evaluation practices and approaches employed among undergraduate nursing educational programs in Ontario, Canada, using a mixed methods approach.Both quantitative and qualitative data were collected through a confidential online survey.The goal of our study is to establish a "picture" of current trends, practices, and approaches related to simulation that is employed within this entire province.An overview of the study findings and recommendations that have potential to make a substantial contribution to the growing evidence for best practices in the science of simulation will be discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".