Organizational Culture Shapes the Adoption and Incorporation of Simulation into Nursing Curricula: A Grounded Theory Study
Bibliographic record
Abstract
Purpose. To create a substantive mid-range theory explaining how the organizational cultures of undergraduate nursing programs shape the adoption and incorporation of mid-to high-level technical fidelity simulators as a teaching strategy within curricula. Method. A constructivist grounded theory was used to guide this study which was conducted in Ontario, Canada, during 2011-12. Semistructured interviews (n = 43) with participants that included nursing administrators, nursing faculty, and simulation leaders across multiple programs (n = 13) informed this study. Additionally, key documents (n = 67) were reviewed. Purposeful and theoretical sampling was used and data were collected and analyzed simultaneously. Data were compared among and between sites. Findings. The organizational elements that shape simulation in nursing (OESSN) model depicts five key organizational factors at the nursing program level that shaped the adoption and incorporation of simulation: (1) leaders working in tandem, (2) information exchange, (3) physical locale, (4) shared motivators, and (5) scaffolding to manage change. Conclusions. The OESSN model provides an explanation of the organizational factors that contributed to the adoption and incorporation of simulation into nursing curricula. Nursing programs that use the OESSN model may experience a more rapid or broad uptake of simulation when organizational factors that impact adoption and incorporation are considered and planned for.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".