Effectiveness of a Game-Based Mobile App for Educating Intensive Critical Care Specialist Nurses in Extracorporeal Membrane Oxygenation Pipeline Preflushing: Quasi-Experimental Trial
Bibliographic record
Abstract
Background: In the context of training specialist nurses and nursing education, a game-based mobile app was used as a simulation to teach intensive critical care specialist nurses the knowledge and skills of extracorporeal membrane oxygenation (ECMO) pipeline preflushing. Objective: This study aimed to evaluate the impact of a game-based mobile app on improving ECMO pipeline preflushing skills in intensive critical care specialist nurses. Methods: A total of 86 intensive critical care specialist nurses who were learning ECMO for the first time were included in this study. The nurses were divided into 2 groups: a control group (n=43) and an experimental group (n=43). Participants in the experimental group used a game-based mobile app for simulation exercises; the control group received no additional intervention. All participants took a theoretical test and a skill operation test at the beginning of the study and 1 week later. The differences in scores between the 2 groups were compared, and the learning curve of the experimental group was observed. Results: The final theoretical test scores (88.44 and 85.02) and skill operation test scores (89.42 and 86.33) of the experimental group and control group, respectively, were significantly higher than those of the initial tests (theoretical test scores: 75.88 and 74.42; skill operation test scores: 75.44 and 75.93; all P<.001). The scores of the final theoretical test (88.44) and the final skill operation test (89.42) in the experimental group were higher than the scores of the control group (85.02; P<.001 and 86.33; P<.001, respectively). Learning curve analysis showed that the experimental group needed an average of 17 operations to master the skill. Conclusions: This study suggests that a game-based mobile app may be more effective for intensive critical care specialist nurses in ECMO pipeline preflushing education than traditional Chinese lecture-practice education.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".