The efficacy of simulation debriefing in developing critical thinking in accelerated baccalaureate nursing students
Bibliographic record
Abstract
Background: Critical thinking is an essential attribute of a nurse. Nursing education which incorporates simulation and debriefing learning activities has an important role to ensure key components of safety and clinical effectiveness are present before nursing students graduate. Aims and methods: To examine, using Quality and Safety Education for Nurses (QSEN) based rubrics, the relationship between simulation debriefing and critical thinking in nursing students enrolled in an accelerated second degree baccalaureate program. Results: Univariate Spearman Rho regression showed a significant direct relationship between critical thinking and all five components of debriefing (allowing reflection on student’s clinical judgement and approach to patient care; feedback received supportive and constructive; feedback helpful to learning; adequate time given to reflect and discuss clinical performance; and helping understand the rational for the actions and responses to performances). Logistic multivariate regression revealed that only three out of the five debriefing components predicted developing stronger critical thinking skills: allowing reflection on student’s clinical judgement and approach to patient care (χ 2 = 34.249, p = .011), adequate time given to reflect and discuss clinical performance (χ 2 = .068, p = .030), and helping understand the rational for the actions and responses to performances (χ 2 = 119.365, p = .001). Conclusions: Debriefing is an important aspect of simulation which helps enhance critical thinking skills in nursing students and thus should be appropriately addressed in education and research.
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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 | 0.019 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".