Effects of different types of feedback on cardiopulmonary resuscitation skills among nursing students–a pilot study
Bibliographic record
Abstract
Background: During the last 20 years there have been different approaches to teaching nurse students cardiopulmonary resuscitation (CPR). Receiving CPR with compressions of adequate depth and frequency, and ventilations of adequate volume improves the chance of survival. The aim of this study was to evaluate effects of different types of feedback on CPR skills among nursing students. Methods: A pilot study with an explorative approach including 30 nurse students. Students was randomized in three groups; 1) instructor-led training followed by self-training without feedback, 2) self-training with visual graphic feedback, and 3) self-training with voice advisory manikin (VAM). Outcomes were correct compression deep, frequency, hand position and release, and correct ventilation volume and flow. If performance was correct to 70%, students were considered to have reached approved level. The students also answered questions about theoretical knowledge about CPR. Results: In technical skills, group 2 had significant higher level of correct ventilation volume compared with the other group. Both group 1 and 3 did not reach the level of 70% correct performance. Group 1 and 2 had significant higher level of correct deep of compressions compared with group 3 which did not reach the 70% level. There was no difference in performance between groups in other parameters. Conclusion: This pilot study suggests that visual graphic feedback is promising and seemed to be more effective than self-training with voice advisory manikin and instructor-led training with followed self-training without feedback.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".