Nurse Faculty Perceptions of Simulation Use in Nursing Education
Bibliographic record
Abstract
In this study nursing faculty perceptions of the implementation of simulation in schools of nursing across Ontario, Canada, were explored using the Q-methodology technique. Following Q-methodology guidelines, 104 statements were collected from faculty and students with exposure to simulation to determine the concourse (what people say about the issue). The statements were classified into six domains, including teaching and learning, access/reach, communication, technical features, technology set-up and training, and comfort/ease of use with technology. They were then refined into 43 final statements for the Q-sample. Next, 28 faculty from 17 nursing schools participated in the Q-sorting process. A by-person factor analysis of the Q-sort was conducted to identify groups of participants with similar viewpoints. Results revealed four major viewpoints held by faculty including: (a) Positive Enthusiasts, (b) Traditionalists, (c) Help Seekers, and (d) Supporters. In conclusion, simulation was perceived to be an important element in nursing education. Overall, there was a belief that clinical simulation requires (a) additional support in terms of the time required to engage in teaching using this modality, (b) additional human resources to support its use, and (c) other types of support such as a repository of clinical simulations to reduce the time from development of a scenario to implementation. Few negative voices were heard. It was evident that with correct support (human resources) and training, many faculty members would embrace clinical simulation because it could support and enhance nursing education.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".