Structured debriefing in nursing simulation: students’ perceptions
Bibliographic record
Abstract
Background and Objective: Debriefing is the strategy which allows a review of a simulated experience or activities in which the participants explore, analyse and summarise their own action and thinking processes, their emotional state and other information which can enhance their performance in real-life situations. The aim of this study was to analyse the students’ perception of the structured debriefing. Methods: Qualitative research developed with 22 students in their fourth year of the nursing degree course. A voluntary, anonymous and confidential questionnaire was applied. The content analysis was based on Bardin. Results: Five categories resulted from the content analysis: the concept; the attributes; the cognitive impact; the psychosocial and the affective impact. They were all grouped into two dimensions: ‘The perception of Structured Debriefing’ and ‘The impact of Structured Debriefing on Students’. Several suggestions emerged such as the continuity of its use and its application to other contexts. Conclusions: The students perceive structured debriefing as an interactive method which allows the consolidation and systematisation of knowledge, the individual and collective reflection and the structured thought. On the other hand, the students mention that structured debriefing enables them to ask questions more ‘openly’ and to have greater proximity among colleagues, making the communication between all participants easier, inter alia, allowing us to mention the positive impact that it has on building skills.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".