Effectiveness of Peyton’s four-step approach on nursing students’ performance in skill-lab training
Bibliographic record
Abstract
Objective: Conveying technical skills to students requires certain techniques and approaches. Peyton’s four-step approach is a model that is becoming increasingly prevalent in medical education. This study was carried out to investigate the effectiveness of Peyton’s four-step approach on nursing students’ outcome in skill-lab training.Methods: The current study was conducted at Faculty of Nursing, Mansoura University. A total of 80 students completed the study. The subjects were divided into two groups: Group (I) the intervention group composed of 40 students; 20 students from first level and 20 students from fourth level who received Peyton’s four-step approach. Group (II) included 40 students considered as a control group composed of 20 students from first level and 20 students from fourth level who received the traditional method of lab training in our faculty. Two procedures were selected to be taught to the students; Intramuscular injection for first level students and arterial puncture for fourth level students.Results: The studied groups significantly exceeded the control group in performance scoring in both intramuscular injection and arterial puncture procedures. The results of the current study also revealed that fourth level students show more acceptances and learning through Peyton’s four-step approach than those of first level students.Conclusions: In the light of the results of our study, we can emphasize that the use of Peyton's four-step approach as a model for teaching practical skills was helpful as reported by nursing students especially for fourth level students.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".