Comparative Effectiveness of Simulation versus Serious Game for Training Nursing Students in Cardiopulmonary Resuscitation: A Randomized Control Trial
Bibliographic record
Abstract
Background. The proper implementation of cardiopulmonary resuscitation (CPR) is crucial in saving patients. Purpose. This study was aimed at evaluating the difference in educating nursing students on CPR when using the traditional simulation training with a mannequin versus a more novel serious game training on the smartphone platform. Methods. This randomized control trial was conducted in 2018-2019. Through purposive sampling, 56 nursing students were selected and randomly assigned to three groups: a simulation-based CPR training, CPR training using a serious game on the smartphone platform, and a control group that received no CPR training. Each student was evaluated pre- and posttraining on CPR knowledge and skill. Results. Both the simulation and serious game training groups increased CPR abilities two weeks after training. The control group did not show improvement in skill or knowledge of CPR. The simulation and serious game intervention groups demonstrated better scores on the knowledge questionnaire and on the CPR skill demonstration in comparison to the control group. However, the simulation group and the serious game group showed no significant difference in knowledge ( 9.55 ± 2.81 vs. 7.77 ± 2.46 ; p = 0.065 ) or CPR skill demonstration ( 27.17 ± 2.81 vs. 25.72 ± 3.98 ; p = 0.988 ). The overall scores for CPR knowledge did not meet minimum expectations (70% score) in either the simulation (47.75%) or serious game (38.85%) group. However, both groups demonstrated adequate CPR skill on demonstration (simulation 87.64% and serious game 83.06%). Conclusions. Both the simulation and serious game training groups were found to increase CPR skill. CPR training would likely benefit from a multimodal approach to education.
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 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 | 0.010 |
| 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".