Influence of simulation design on stress, anxiety and self‐confidence of nursing students: Systematic review with meta‐analysis
Bibliographic record
Abstract
AIM: To evaluate the simulation design characteristics that may influence the stress, anxiety and self-confidence of undergraduate nursing students during learning. DESIGN: Systematic review with meta-analysis. DATA SOURCES: Searchers were conducted in October 2020 and updated in August 2022 in the databases CENTRAL, CINAHL, Embase®, ERIC, LILACS, MEDLINE, PsycINFO®, Scopus and Web of Science, PQDT Open (ProQuest), BDTD, Google Scholar and specific journals on simulation. REVIEW METHODS: This review was conducted according to the recommendations of Cochrane Handbook for Systematic Reviews and reported according to the PRISMA Statement. Experimental and quasi-experimental studies that compared the effect of simulation on stress, anxiety and self-confidence of nursing students were included. The selection of studies and data extraction was performed independently by two reviewers. Simulation information was collected as prebriefing, scenario, debriefing, duration, modality, fidelity and simulator. Data summarization was performed by qualitative synthesis and meta-analytical methods. RESULTS: Eighty studies were included in the review, and most reported in detail the structure of the simulation, contemplating prebriefing, scenario, debriefing and the duration of each step. In subgroup meta-analysis, the presence of prebriefing, duration of more than 60 min and high-fidelity simulations helped reduce anxiety, while the presence of prebriefing and debriefing, duration, immersive clinical simulation modalities and procedure simulation, high-fidelity simulations and use of mannequins, standardised patients and virtual simulators, contributed to greater students' self-confidence. CONCLUSIONS: Different modulations of simulation design components imply reduction of anxiety and increased self-confidence in nursing students, especially highlighting the quality of the methodological report of simulation interventions. RELEVANCE TO CLINICAL PRACTICE: These findings help to support the need of more rigorous methodology in simulation designs and research methods. Consequently, impact on the education of qualified professionals prepared to work in clinical practice. No Patient or Public Contribution.
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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 | 0.018 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".