Effect of simulation on stress, anxiety, and self-confidence in nursing students: Systematic review with meta-analysis and meta-regression
Bibliographic record
Abstract
INTRODUCTION: Simulation is a promising strategy in health education, with evidence of importance for learning, but the available systematic reviews are still inconclusive about the effect of the strategy on stress, anxiety, and self-confidence of nursing students, which impact the adherence to and sustainment of this strategy. Thus, better evidence is needed of the impact of simulation on these competences, essential for health professional education. OBJECTIVE: To evaluate the effect of simulation-based experiences on stress, anxiety, self-confidence and learning of undergraduate nursing students compared to conventional teaching strategies or no intervention. DESIGN: Systematic review with meta-analysis and meta-regression. DATA SOURCES: The databases used included: CENTRAL, CINAHL, Embase®, ERIC, LILACS, MEDLINE, PsycINFO®, SCOPUS and Web of Science. Additional searches occurred in PQDT Open (ProQuest), BDTD, Google Scholar and journals with a specific scope in clinical simulation. REVIEW METHODS: This study was conducted by the recommendations of the Cochrane Handbook for Systematic Reviews of Interventions. Experimental and quasi-experimental studies that compared the effects of simulation on stress, anxiety, and self-confidence of nursing students were included. Study selection and data extraction steps were performed independently by two reviewers. Critical appraisal of the studies was managed by means of the risk of bias tools RoB 2 and ROBINS-I, and quality of evidence by means of the GRADE tool. Data summarization was performed by qualitative synthesis with descriptive analysis and quantitative synthesis by meta-analytic methods and meta-regression. RESULTS: = 20.96%). CONCLUSION: Simulation is an effective strategy for reducing anxiety and increasing self-confidence compared to conventional teaching strategies. Results are still inconclusive for stress. The use of simulation-based experiences in nursing education obtains positive results on anxiety and self-confidence in students, providing support for its implementation in undergraduate curricula to improve the education of qualified nurses. REGISTRATION NUMBER: CRD42020206077. TWEETABLE ABSTRACT: Simulation is an effective teaching strategy for reducing anxiety and increasing self-confidence in learning.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".