Effect of Computer Debriefing on Acquisition and Retention of Learning After Screen-Based Simulation of Neonatal Resuscitation: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Debriefing is key in a simulation learning process. OBJECTIVE: This study focuses on the impact of computer debriefing on learning acquisition and retention after a screen-based simulation training on neonatal resuscitation designed for midwifery students. METHODS: Midwifery students participated in 2 screen-based simulation sessions, separated by 2 months, session 1 and session 2. They were randomized in 2 groups. Participants of the debriefing group underwent a computer debriefing focusing on technical skills and nontechnical skills at the end of each scenario, while the control group received no debriefing. In session 1, students participated in 2 scenarios of screen-based simulation on neonatal resuscitation. During session 2, the students participated in a third scenario. The 3 scenarios had an increasing level of difficulty, with the first representing the baseline level. Assessments included a knowledge questionnaire on neonatal resuscitation, a self-efficacy rating, and expert evaluation of technical skills as per the Neonatal Resuscitation Performance Evaluation (NRPE) score and of nontechnical skills as per the Anaesthetists' Non-Technical Skills (ANTS) system. We compared the results of the groups using the Mann-Whitney U test. RESULTS: A total of 28 midwifery students participated in the study. The participants from the debriefing group reached higher ANTS scores than those from the control group during session 1 (13.25 vs 9; U=47.5; P=.02). Their scores remained higher, without statistical difference during session 2 (10 vs 7.75; P=.08). The debriefing group had higher self-efficacy ratings at session 2 (3 vs 2; U=52; P=.02). When comparing the knowledge questionnaires, the significant baseline difference (13 for debriefing group vs 14.5 for control group, P=.05) disappeared at the end of session 1 and in session 2. No difference was found for the assessment of technical skills between the groups or between sessions. CONCLUSIONS: Computer debriefing seems to improve nontechnical skills, self-efficacy, and knowledge when compared to the absence of debriefing during a screen-based simulation. This study confirms the importance of debriefing after screen-based simulation. TRIAL REGISTRATION: ClinicalTrials.gov NCT03844009; https://clinicaltrials.gov/ct2/show/NCT03844009.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".