Application of a “nursing education cloud platform”-based combined and phased training model in the education of standardized-training nurses: A quasi-experimental study

The evolution of nursing education has rendered traditional standardized-training models increasingly inadequate, primarily due to their inflexible curricula, limited personalized instruction, and delayed feedback loops. While stage-based training models offer improved coherence through structured planning, they encounter difficulties in resource integration and real-time interaction. Contemporary advancements in cloud computing and Internet of Things technologies present novel opportunities for educational reform. Nursing Education Cloud Platform (NECP)-based systems have demonstrated efficacy in medical education, particularly in efficient resource management, data-driven decision-making, and the design of adaptable learning pathways. Despite the nascent implementation of cloud platforms in standardized nurse training, the sustained impact on multifaceted competencies, including professional identity and clinical reasoning, warrants further investigation. The primary objective of this investigation was to assess the effectiveness of a NECP-integrated, phased training model in enhancing standardized-training nurses' theoretical comprehension, practical competencies, professional self-perception, and clinical decision-making capabilities, while also examining its potential to refine nursing education methodologies. This quasi-experimental, non-randomized controlled trial evaluated the impact of a NECP-based training program. The study encompassed an experimental group (n = 56, receiving cloud platform-based training from September 2021 to August 2022) and a control group (n = 56, undergoing traditional training from September 2020 to August 2021). Group assignment was determined by the hospital's annual training schedule, thus employing a natural grouping based on the time period. Propensity score matching was utilized to mitigate baseline characteristic imbalances. The intervention's effects were assessed across several domains, including theoretical knowledge, operational skills, professional identity, and clinical reasoning abilities. ANCOVA was employed to account for temporal covariates. The experimental group scored significantly higher than the control group in theoretical knowledge (88.70 ± 5.07 vs 75.55 ± 9.01, P < .05), operational skills (94.27 ± 2.04 vs 90.95 ± 3.69, P < .05), professional identity (73.18 ± 10.18 vs 62.54 ± 15.48, P < .05), and clinical reasoning ability (60.95 ± 8.90 vs 51.09 ± 12.28, P < .05). The integration of the "NECP" with a phased training model demonstrates efficacy in augmenting nurses' competencies. However, the potential for selection bias, inherent in the non-randomized design, warrants careful consideration in the interpretation of these findings. Further investigation, specifically through multicenter longitudinal studies, is recommended to ascertain the generalizability of these results.

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Publication Details

Journal
Medicine
Published
2025-08-22
DOI
https://doi.org/10.1097/md.0000000000044138
Citations
1
Primary Topic
Simulation-Based Education in Healthcare
Type
article
Field-Weighted Citation Impact
1.16
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article

Application of a “nursing education cloud platform”-based combined and phased training model in the education of standardized-training nurses: A quasi-experimental study

Shaoyi Hu, Huaxi Liu, Yuan Yuan, H.K. Tang
1 citations
Medicine
Simulation-Based Education in Healthcare
1.16
article

Application of a “nursing education cloud platform”-based combined and phased training model in the education of standardized-training nurses: A quasi-experimental study

Shaoyi Hu, Huaxi Liu, Yuan Yuan, H.K. Tang
article en
1 citations

Abstract

The evolution of nursing education has rendered traditional standardized-training models increasingly inadequate, primarily due to their inflexible curricula, limited personalized instruction, and delayed feedback loops. While stage-based training models offer improved coherence through structured planning, they encounter difficulties in resource integration and real-time interaction. Contemporary advancements in cloud computing and Internet of Things technologies present novel opportunities for educational reform. Nursing Education Cloud Platform (NECP)-based systems have demonstrated efficacy in medical education, particularly in efficient resource management, data-driven decision-making, and the design of adaptable learning pathways. Despite the nascent implementation of cloud platforms in standardized nurse training, the sustained impact on multifaceted competencies, including professional identity and clinical reasoning, warrants further investigation. The primary objective of this investigation was to assess the effectiveness of a NECP-integrated, phased training model in enhancing standardized-training nurses' theoretical comprehension, practical competencies, professional self-perception, and clinical decision-making capabilities, while also examining its potential to refine nursing education methodologies. This quasi-experimental, non-randomized controlled trial evaluated the impact of a NECP-based training program. The study encompassed an experimental group (n = 56, receiving cloud platform-based training from September 2021 to August 2022) and a control group (n = 56, undergoing traditional training from September 2020 to August 2021). Group assignment was determined by the hospital's annual training schedule, thus employing a natural grouping based on the time period. Propensity score matching was utilized to mitigate baseline characteristic imbalances. The intervention's effects were assessed across several domains, including theoretical knowledge, operational skills, professional identity, and clinical reasoning abilities. ANCOVA was employed to account for temporal covariates. The experimental group scored significantly higher than the control group in theoretical knowledge (88.70 ± 5.07 vs 75.55 ± 9.01, P < .05), operational skills (94.27 ± 2.04 vs 90.95 ± 3.69, P < .05), professional identity (73.18 ± 10.18 vs 62.54 ± 15.48, P < .05), and clinical reasoning ability (60.95 ± 8.90 vs 51.09 ± 12.28, P < .05). The integration of the "NECP" with a phased training model demonstrates efficacy in augmenting nurses' competencies. However, the potential for selection bias, inherent in the non-randomized design, warrants careful consideration in the interpretation of these findings. Further investigation, specifically through multicenter longitudinal studies, is recommended to ascertain the generalizability of these results.

MedicineVol. 104(34)
Army Medical University (CN), Institute of Scientific and Technical Research for Defense (AR), The Affiliated Yongchuan Hospital of Chongqing Medical University (CN), Southwest Hospital (CN), S.P.E.C.I.E.S. (US), Chongqing Medical University (CN)
Openalex Percentile: Top 18%
Simulation-Based Education in Healthcare
1.16
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