Examining Nurse Educator Experience and Understanding of Simulation Pedagogy within the Practical Nurse Curriculum
Bibliographic record
Abstract
Simulation-based nurse education provides a way to foster skill development, decision making, and the ability to think critically when faced with rapidly changing situations in the clinical setting. Utilizing simulation as an effective teaching and learning strategy requires the careful orchestration of instructional methods and the appropriate application of simulation technologies. It should involve educational experiences that are reflective of the healthcare industry’s best practices. The aim of this qualitative research study was to examine and understand how nurse educators experience and understand the various dimensions of simulation pedagogy as a teaching and learning strategy within the practical nursing curriculum. Eight nurse educators who work in a practical nursing program at a community college in Ontario, Canada participated in semi-structured interviews. The major findings include: i) Participants said they were initially unsure how to teach with simulations and it took time and practice before they felt proficient; ii) Participants with simulation-based certifications reported that this form of professional development improved both their self-confidence and their teaching practices; iii) Participants who were not certified were unfamiliar with the INACSL Simulation Standards of Best PracticeTM relied on their own intuitions and past instructional practices when implementing simulation experiences; iv) Part of their discomfort in teaching in a simulated nurse environment was related to technology hesitancy; v) None of the participants felt that they had received necessary support from administrators; vi) Participants felt that receiving training through certification was necessary to fully understand the pedagogy and to be an effective simulation educator; and vii) creating opportunities for peer mentorship and coaching would be a productive step to further strengthen their understanding and implementation of simulation pedagogies. Recommendations are identified not only for the educator, but also administrators and postsecondary institutions that offer simulation education. This study sheds new light on the professional development needs of nurse educators. It is hoped that the study’s findings will ultimately lead to the improved use of simulation pedagogies in schools of nursing.
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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 | 0.007 | 0.022 |
| 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 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".