Crossover design in triage education: the effectiveness of simulated interactive vs. routine training on student nurses’ performance in a disaster situation
Bibliographic record
Abstract
INTRODUCTION: This study investigates the effectiveness of incorporating simulated interactive guidelines in nursing students' performance during disaster situations, compared to routine training. METHOD: This study was a crossover design with pre-and post-tests for two groups. Each group consisted of 60 students selected using the census method. SIG and routine (Face-to-Face) training sessions were conducted as a crossover design. Triage knowledge questionnaires were used in the pretest to assess triage knowledge. An OSCE test was administered in the posttest to assess student performance, followed by a triage skills questionnaire. Both questionnaires were highly reliable, as indicated by Cronbach's alpha coefficients (0.9 and 0.95, respectively). Statistical analysis was performed using SPSS version 26 software at a significance level 0.05. RESULT: The chi-square test showed that the two groups were homogeneous regarding age. Regarding knowledge level, both groups were homogeneous before the intervention (P = 0.99). Nevertheless, the results of the OSCE test showed that the students in Group A had a higher level of skill than the students in Group B (93% versus 70%). Also, 18% of the students in group B had low skills. DISCUSSION: The study found that student outcomes improved in both groups receiving SIG, suggesting that interaction and simulation improve learning. However, gamification is an ideal precursor to learning and not a substitute for education. Therefore, gamification should not be used as a stand-alone teaching method. CONCLUSIONS: The crossover study found that simulators and games should not be considered stand-alone teaching methods but can contribute to learning sustainability when used alongside instruction.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".