Effectiveness of the combined LSPPDM and simulation teaching model in neonatal nursing intern training
Xiaoshan Huang1, Xiaoyan Ye1, Huilan Yang1
1Department of Neonatal Intensive Care Unit, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Objective:
To explore the effects of applying the Learn-See-Practice-Prove-Do-Maintain (LSPPDM) learning model in combination with simulation-based teaching methods to neonatal nurse teaching practices.
Methods:
This is a historical controlled non-randomized quasi-experimental study. The combination LSPPDM-simulation teaching model was officially implemented into the study hospital's nursing internship educational program in 2023. A control group of 72 interns received instruction by conventional methods between August and December 2022; an observation group of 71 interns received instruction via the LSPPDM-simulation teaching combination model between May 2023 and April 2024. The self-learning ability scale, humanistic care ability evaluation scale, Core Competency Scale for Registered Nurses in China, and final exam scores of the two groups were compared.
Results:
The observation group scored significantly higher than the control group with respect to the following aspects: autonomous learning (70.18 ± 7.11 vs. 66.45 ± 8.64, p = 0.001), care ability (127.12 ± 4.23 vs. 121.28 ± 11.16, p = 0.001), nursing core competence (163.33 ± 21.55 vs. 144.63 ± 19.09, p = 0.001), final examination (82.36 ± 3.35 vs. 79.09 ± 6.87, p = 0.001), and satisfaction with teaching methods (12.03 ± 0.56 vs. 9.34 ± 0.35, p < 0.001).
Conclusion:
This study demonstrates that the integration of LSPPDM with simulation-based teaching significantly enhances the self-directed learning, humanistic care, and core competencies of nursing students during their neonatal clinical rotations, offering a novel model for pediatric nursing education. Future research should include multicenter, large-sample, randomized controlled trials to further validate the effectiveness and scalability of this approach.
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