Nurse Faculty Perceptions of Simulation Use in Nursing Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it