Speaking up for patient safety: Bringing simulation to the classroom
Bibliographic record
Abstract
• Team communication and interprofessional teams is essential for patient safety. • In-class simulations provided timely application of theory in a safe environment. • Scaffolding lecture activities and simulation may enhance skills in clinical practice. Nursing students require leadership skills and the ability to communicate in interprofessional teams to ensure safe patient care. Breakdown in effective team communication has been identified as the leading cause contributing to medical errors. Team communication and teamwork tools have been developed to improve patient safety. A scaffolded teaching approach was implemented including the use of simulation in the classroom . During a 3-hour lecture, team communication and teamwork tools were introduced in a fourth-year nursing leadership course, followed by an online discussion board and an in-class simulated based experience (SBE). Students valued the opportunity to practice speaking up prior to utilizing in the clinical area. Integration of the in-class SBE supported student learning to speak up. Debrief sessions facilitated reflection improving nursing students’ leadership and communication skills. In-classroom simulations utilizing team communication supports didactic lectures providing students the opportunity to practice team communication tools in a safe environment and may enhance speaking up skills in the clinical environment.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".