Innovative Methodologies to Teach Patient Safety in Undergraduate Nursing: Scoping Review
Bibliographic record
Abstract
Objective: This work sought to identify the innovative methodologies used to teach patient safety in undergraduate Nursing. Materials and method: this is a scoping review conducted according to the recommendations by the Joanna Briggs Institute Reviewers’ Manual, through the databases: Medline/PubMed, Cumulative Index of Nursing and Allied Health, Scopus, Web of Science, Education Resources Information Center, Latin American and Caribbean Literature on Health Sciences, Catálogo de Tesis de la Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, The National Library of Australia’s Academic Archive Online, Digital Access to Research Theses Europe E-Theses Portal, Electronic Theses Online Service, Repositório Científico de Acesso Aberto de Portugal, National ETD Portal, and Theses Canada. Results: the study included 19 studies, most of descriptive type (n = 8; 42.1 %) and quasi-experimental (n = 7; 36.8 %) with quantative approach (n = 11; 57.9 %), conducted in the United States (n = 4; 21.1 %), in 2013 (n = 4; 21.1 %) and 2017 (n = 4; 21.1 %). The innovative methodologies used to teach patient safety in undergraduate Nursing that were highlighted included the scenario of the care practice simulated in the laboratory (n = 14; 73.7 %) and educational videos (n = 7; 36.8 %). Conclusions: it was identified that innovative methodologies used to teach patient safety in undergraduate Nursing were simulation, videos, staging/role playing, and films, all applied in the classroom teaching modality.
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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 | 0.059 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.029 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".