“Negotiating, Navigating, and Networking”: Three Strategies Used by Nursing Leaders to Shape the Adoption and Incorporation of Simulation into Nursing Curricula—A Grounded Theory Study
Bibliographic record
Abstract
Background. Implementing simulation requires a substantial commitment of human and financial resources. Despite this, little is known about the strategies used by academic nursing leaders to facilitate the implementation of a simulation program in nursing curricula. Methods. A constructivist grounded theory study was conducted within 13 nursing programs in Ontario, Canada. Perspectives of key stakeholders (n = 27) including nursing administrators (n = 6), simulation leaders (n = 9), and nursing faculty (n = 12) were analyzed using the constant comparison method. Results. Nursing leaders, specifically nursing administrators and simulation leaders who successfully led the adoption and incorporation of simulation into nursing curricula, worked together and utilized negotiating, navigating, and networking strategies that impacted the adoption and incorporation of simulation into nursing curricula. Conclusions. Strategies that were found to be useful when planning and executing the adoption and incorporation of an innovation, specifically simulation, into nursing curricula provide practical approaches that may be helpful to nurse leaders when embarking upon an organizational change.
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 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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".