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Record W4408295427 · doi:10.1016/j.teln.2025.02.023

Speaking up for patient safety: Bringing simulation to the classroom

2025· article· en· W4408295427 on OpenAlexaff
Robert Catena, Heather MacLean

Bibliographic record

VenueTeaching and learning in nursing · 2025
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMount Royal University
Fundersnot available
KeywordsPsychologyPatient safetyPolitical scienceLawHealth care

Abstract

fetched live from OpenAlex

• 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.387
Teacher spread0.368 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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