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陆军军医大学第二附属医院急诊科,重庆 400037
谭丹,Email:107284058@qq.com
收稿:2025-01-22,
纸质出版:2025-10-20
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王怡, 曾伟, 杜巧, 等. 基于“P4”医学理念的干休所和疗养院医疗人员岗位培训需求因子分析及培训模型构建[J]. 中华医学教育探索杂志, 2025, 24(10): 1327-1334.
Wang Yi, Zeng Wei, Du Qiao, et al. Factor analysis of job training needs and construction of a training model based on "P4 medicine" for medical staff in military rest homes and sanatoriums[J]. Chinese journal of medical education research, 2025, 24(10): 1327-1334.
王怡, 曾伟, 杜巧, 等. 基于“P4”医学理念的干休所和疗养院医疗人员岗位培训需求因子分析及培训模型构建[J]. 中华医学教育探索杂志, 2025, 24(10): 1327-1334. DOI: 10.3760/cma.j.cn116021-20250122-02130.
Wang Yi, Zeng Wei, Du Qiao, et al. Factor analysis of job training needs and construction of a training model based on "P4 medicine" for medical staff in military rest homes and sanatoriums[J]. Chinese journal of medical education research, 2025, 24(10): 1327-1334. DOI: 10.3760/cma.j.cn116021-20250122-02130.
目的
2
探讨基于“P4”医学理念的岗位培训需求因子分析方法,为优化干休所及疗养院医疗人员继续教育体系提供循证依据。
方法
2
采用横断面研究设计,以参加新桥医院岗位培训的95名干休所及疗养院(帮扶体系内的多家干休所和疗养院)医疗人员为研究对象,基于“P4”框架编制结构化问卷。通过两轮德尔菲专家函询筛选46项核心指标,采用主成分分析法提取公因子,结合最大方差旋转与熵权法构建需求层次模型。
结果
2
共提取4个公因子,累计方差贡献率81.564%。主因子1“老年医学知识动态更新”(31.83%)涵盖老年共病管理、危急值识别等临床核心能力;主因子2“应急反应性与个性化服务”(26.05%)聚焦军事医疗法规、应急救治及定制化干预;主因子3“多团队合作与领导能力构建”(12.41%)强调战备状态团队重组机制;主因子4“智能技术整合应用”(11.29%)体现数据驱动决策需求。熵权法显示,前10%高权重指标中医学知识动态更新(急救技术权重0.050)以及AI应用(权重0.019)等。
结论
2
“P4”医学理念可有效解析基层医疗人员的复合型培训需求,其“预测-规范-协作-创新”四维结构为课程设计提供理论框架。建议构建分层递进的课程模块,重点强调技术赋能机制,以提升老年健康服务的精准化与可持续性。
Objective
2
To explore a factor analysis method for job training needs based on "P4 medicine"
and to provide evidence-based support for optimizing continuing education systems for medical staff in military rest homes and sanatoriums.
Methods
2
A cross-sectional study was conducted with 95 medical staff from military rest homes and sanatoriums within the support system who participated in job training at Xinqiao Hospital. A structured questionnaire was developed based on the "P4 medicine" framework. After two rounds of Delphi expert consultation
46 core indicators were selected. A principal component analysis was used to extract common factors
and a model of hierarchical needs was constructed by combining varimax rotation and entropy weight methods.
Results
2
Four common factors were extracted
accounting for 81.564% cumulative variance. The first factor "dynamic updating of geriatric medical knowledge" (31.83%) covered clinical core competencies in managing geriatric comorbidities and recognizing critical values. The second factor "emergency responsiveness and personalized care" (26.05%) focused on military medical regulations
emergency treatment protocols
and tailored interventions. The third factor "multidisciplinary collaboration and leadership development" (12.41%) emphasized team reorganization in combat-ready scenarios. The fourth factor "integration of intelligent technologies" (11.29%) reflected data-driven decision-making needs. Entropy weight analysis highlighted dynamic medical knowledge updates (e.g.
emergency skills with a weight of 0.050) and AI applications with a weight of 0.019 among the top 10% high-weight indicators.
Conclusions
2
The "P4 medicine" effectively delineates the multidimensional training needs of grassroots medical personnel. Its four-dimensional structure (predictive
normative
collaborative
and innovative) provides a theoretical framework for curriculum design. We recommend constructing tiered training modules prioritizing technology-enabled mechanisms to enhance the precision and sustainability of geriatric healthcare services.
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