Effectiveness of role-play in hazard prediction training for nursing students: A randomized controlled trial
Bibliographic record
Abstract
Objective: Patient safety education in nursing education is a matter of worldwide concern. Various simulation training has been introduced into patient safety education. It is difficult for nursing students to fully understand the situation of scenarios in simulation training. Having attempted to solve the problem, educators have used the illustrations, videos and manikins. Role-play is widely used in simulation training in nursing education. As to patient safety education, few randomized controlled trials (RCTs) have reported the effectiveness of role-play compared with traditional situational presentation methods such as illustrations and videos. Therefore, we performed an RCT to examine the effectiveness of role-play compared with illustrations using hazard prediction training (Kiken-Yochi-Training; KYT) which is one of simulation training widely used in Japan.Methods: The participants were 94 second-year nursing students. All students were randomly allocated to a role-play group (R-group) or an illustrations group (I-group). Participants were asked to complete the risk sensitivity scale for nursing students before and after KYT. After KYT, all participants were asked to undergo a hazard prediction test. Linear mixed models were used to examine differences in the scale scores within and between intervention groups.Results: Participants in the R-group had a significantly higher number of hazard prediction points than those in the I-group (R-group: 2.50 ± 1.07, I-group: 1.77 ± 0.95, p = .001). Scores were significantly increased on the risk sensitivity scale for nursing students in both groups, while no significant differences were seen in score increments between the groups.Conclusions: The results of our randomized study showed that effectiveness of role-play in hazard prediction training in university-based nursing education. Our study also suggested KYT increased risk sensitivity among nursing students, and that this effect was not affected by the situation presentation method, role-play or illustration.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".