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微视频追根溯源培训模式在急诊科护士新发传染病培训中的应用
引用本文:李彬霞,张育红,颜美琼,冯丽,王萍,郑芳莉,秦佳楠.微视频追根溯源培训模式在急诊科护士新发传染病培训中的应用[J].中华医学教育探索杂志,2024,23(1):108-113.
作者姓名:李彬霞  张育红  颜美琼  冯丽  王萍  郑芳莉  秦佳楠
作者单位:1.复旦大学附属中山医院护理部,上海 200032;2.复旦大学附属中山医院急诊科,上海 200032;3.复旦大学护理学院,上海 200032
基金项目:复旦大学-复星护理科研基金(FNF202075);复旦大学附属中山医院学科建设(xk-082-010)
摘    要:目的 探讨微视频追根溯源培训模式应用于新发传染病(emerging infectious diseases, EID)知识培训对急诊科护士自主学习能力和学习效果的影响。方法 本研究从EID的历史发展视角制定培训内容,以微视频归类模块、基于微信群分期推送的自主学习形式构建培训方案。采用方便抽样法选取上海市某三级甲等综合医院85名急诊科护士(n=85),进行微视频追根溯源培训,通过含有启发性思考、微视频学习、要点巩固、要点解析、延伸拓展五个环节的11期模块对其进行EID知识培训。比较干预前后急诊科护士自主学习能力和EID知信行水平,并调查其对该培训模式的满意度评价。使用SPSS 20.0及GraphPad Prism 9.3.0进行四分位数(Q25MQ75)的非参数Wilcoxon配对检验。结果 急诊科护士接受培训前后,其自主学习能力整体(Z=-4.69,P<0.001)及自我动机概念(Z=-4.48,P<0.001)、任务分析(Z=-4.06,P<0.001)、自我监控与调节(Z=-4.50,P<0.001)、自我评价(Z=-4.82,P<0.001)各维度得分差异均有统计学意义,培训后高于培训前;其EID知信行整体(Z=-4.65,P<0.001)及知识(Z=-4.04,P<0.001)、态度(Z=-3.24,P=0.001)、行为(Z=-4.05,P<0.001)各维度得分差异均有统计学意义,培训后高于培训前。急诊科护士对该培训模式满意度评价较高(各条目满意度均>90%)。结论 微视频追根溯源培训模式能有效提高急诊科护士自主学习能力和EID知信行水平,培训满意度较高,可尝试推广使用。

关 键 词:新发传染病(EID)  护士  微视频  追根溯源  自主学习
收稿时间:2022/7/1 0:00:00

Application of micro-video tracing training model to emerging infectious diseases knowledge training in emergency department nurses
Li Binxi,Zhang Yuhong,Yan Meiqiong,Feng Li,Wang Ping,Zheng Fangli,Qin Jia'nan.Application of micro-video tracing training model to emerging infectious diseases knowledge training in emergency department nurses[J].Chinese Journal of Medical Education Research,2024,23(1):108-113.
Authors:Li Binxi  Zhang Yuhong  Yan Meiqiong  Feng Li  Wang Ping  Zheng Fangli  Qin Jia'nan
Institution:1.Department of Nursing, Zhongshan Hospital, Fudan University, Shanghai 200032, China;2.Department of Emergency, Zhongshan Hospital, Fudan University, Shanghai 200032, China;3.School of Nursing, Fudan University, Shanghai 200032, China
Abstract:Objective To explore the impact of applying micro-video tracing training model to emerging infectious diseases (EIDs) knowledge training on the autonomous learning ability and learning outcomes among emergency department nurses.Methods In this study, the training content was developed from the perspective of the historical development of EIDs. The training program was constructed in the form of a micro-video classification module and periodic WeChat group push-based autonomous learning. Convenience sampling was used to select 85 emergency nurses (n=85) from a grade A tertiary general hospital in Shanghai for micro-video tracing training. The nurses were provided with EIDs knowledge training using a module consisting of 11 episodes, each of which included 5 sessions, i.e., enlightening thinking, micro-video learning, key point consolidation, key point analysis, and extension and expansion. After the intervention, the nurses were compared for their autonomous learning ability and their knowledge, belief, and action levels of EIDs, and their degree of satisfaction with the training model was evaluated. SPSS 20.0 and GraphPad Prism 9.3.0 were used for non-parametric Wilcoxon paired tests of quartiles (Q25, M, and Q75).Results After the training, the emergency department nurses had significantly higher scores in overall autonomous learning ability (Z=-4.69, P<0.001) and in each dimension: concept of self-motivation (Z=-4.48, P<0.001), task analysis (Z=-4.06, P<0.001), self-monitoring and regulation (Z=-4.50, P<0.001), and self-evaluation (Z=-4.82, P<0.001); they had significantly higher scores in overall knowledge, belief, and action levels of EIDs (Z=-4.65, P<0.001) and in each dimension: knowledge (Z=-4.04, P<0.001), belief (Z=-3.24, P=0.001), and action (Z=-4.05, P<0.001). Emergency department nurses had a high degree of satisfaction with the training model (degree of satisfaction for each item >90%).Conclusions The micro-video tracing training model can effectively improve the autonomous learning ability of emergency department nurses and their knowledge, belief, and action levels of EIDs, with a high degree of satisfaction among the nurses, making it promising for wide use.
Keywords:Emerging infectious disease  Nurse  Micro-video  Trace  Autonomous learning
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