The value of simulation debriefing in launching reduced anxiety and improved self-confidence in the clinical setting for accelerated baccalaureate nursing students
Bibliographic record
Abstract
Background and objective: The development of self-confidence is an essential element of a nurse in the clinical setting. Nursing educators discuss the addition of simulation and debriefing into learning activities, which play a central role in identifying the fundamental elements of safety and clinical efficiency.Methods: Quality and Safety Education for Nurses (QSEN) competencies are used to examine the data supporting the effectiveness of simulation debriefing in nursing students registered in a fast-tracked baccalaureate program. This novel approach allows one to quantitatively measure the relationship between simulation debriefing, self-confidence and reduced anxiety.Results: Univariate Spearman Rho regression displays a significant positive correlation between reduced anxiety, self-confidence, and debriefing. The feedback received is encouraging, productive, and effective to learning. Logistic multivariate regression reveals debriefing mechanisms predict developing self-confidence and reducing anxiety, allowing the likeness on student’s clinical judgement and methodology to patient care (χ2 = 34.249, p = .011), sufficient time being provided to reflect and review clinical performance (χ2 = 0.68, p = .30) and identifying the justification for the actions and responses (χ2 = 119.365, p = .001).Conclusions: Debriefing is a central element that can be applied as a teaching strategy during simulation. This study offers further understanding of the role of debriefing in enhancing self-confidence and reducing anxiety in nursing students. This is a critical learning component and ought to be applicably focused in nursing education.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".