目的分析正中开胸心脏瓣膜置换患者应用基于健康行为改变整合理论(integrated theory of health behavior change,ITHBC)健康教育的效果。方法选取2022年7月—2023年6月广西壮族自治区人民医院收治的正中开胸心脏瓣膜置换患者120例,将...目的分析正中开胸心脏瓣膜置换患者应用基于健康行为改变整合理论(integrated theory of health behavior change,ITHBC)健康教育的效果。方法选取2022年7月—2023年6月广西壮族自治区人民医院收治的正中开胸心脏瓣膜置换患者120例,将其根据不同健康教育方式分为对照组(60例,常规健康教育)与观察组(60例,常规健康教育+基于ITHBC理论的健康教育)。2组均干预4个月。比较2组护理的依从性、满意度(干预后),干预前后心理状态、生活质量、自我管理能力。结果观察组各项指标依从率(合理饮食96.67%、定期复诊95.00%、作息规律96.67%、情绪稳定98.33%、按医嘱用药96.67%)均高于对照组(合理饮食86.67%、定期复诊83.33%、作息规律85.00%、情绪稳定86.67%、按医嘱用药86.67%)(P<0.05)。干预后,观察组满意度高于对照组(P<0.05)。相较于干预前,2组干预后的焦虑自评量表(self-rating anxiety scale,SAS)、抑郁自评量表(self-rating depression scale,SDS)评分、生活质量各项评分降低,且组间进行对比,观察组较低(P<0.05)。干预后,2组自我管理能力各项评分均升高,且观察组较对照组低(P<0.05)。结论正中开胸心脏瓣膜置换患者应用基于ITHBC理论的健康教育,可有助于提高患者依从性,改善生活质量,增强自我管理能力,消除不良情绪,进而获得患者认可,且有助于为临床改善正中开胸心脏瓣膜置换患者预后提供参考及依据。展开更多
We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algori...We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algorithmic information theory (AIT), in general, and algorithmic randomness and Kolmogorov complexity (KC), in particular. The processing and recognition tasks addressed include data discrimination and multilayer open set data categorization, change detection, data aggregation, clustering and data segmentation, data selection and link analysis, data cleaning and data revision, and prediction and identification of critical states. The unifying theme throughout the paper is that of “compression entails comprehension”, which is realized using the interrelated concepts of randomness vs. regularity and Kolmogorov complexity. The constructive and all encompassing active learning (AL) methodology, which mediates and supports the above theme, is context-driven and takes advantage of statistical learning, in general, and semi-supervised learning and transduction, in particular. Active learning employs explore and exploit actions characteristic of closed-loop control for evidence accumulation in order to revise its prediction models and to reduce uncertainty. The set-based similarity scores, driven by algorithmic randomness and Kolmogorov complexity, employ strangeness / typicality and p-values. We propose the application of the IIM framework to critical states prediction for complex physical systems;in particular, the prediction of cyclone genesis and intensification.展开更多
文摘目的分析正中开胸心脏瓣膜置换患者应用基于健康行为改变整合理论(integrated theory of health behavior change,ITHBC)健康教育的效果。方法选取2022年7月—2023年6月广西壮族自治区人民医院收治的正中开胸心脏瓣膜置换患者120例,将其根据不同健康教育方式分为对照组(60例,常规健康教育)与观察组(60例,常规健康教育+基于ITHBC理论的健康教育)。2组均干预4个月。比较2组护理的依从性、满意度(干预后),干预前后心理状态、生活质量、自我管理能力。结果观察组各项指标依从率(合理饮食96.67%、定期复诊95.00%、作息规律96.67%、情绪稳定98.33%、按医嘱用药96.67%)均高于对照组(合理饮食86.67%、定期复诊83.33%、作息规律85.00%、情绪稳定86.67%、按医嘱用药86.67%)(P<0.05)。干预后,观察组满意度高于对照组(P<0.05)。相较于干预前,2组干预后的焦虑自评量表(self-rating anxiety scale,SAS)、抑郁自评量表(self-rating depression scale,SDS)评分、生活质量各项评分降低,且组间进行对比,观察组较低(P<0.05)。干预后,2组自我管理能力各项评分均升高,且观察组较对照组低(P<0.05)。结论正中开胸心脏瓣膜置换患者应用基于ITHBC理论的健康教育,可有助于提高患者依从性,改善生活质量,增强自我管理能力,消除不良情绪,进而获得患者认可,且有助于为临床改善正中开胸心脏瓣膜置换患者预后提供参考及依据。
文摘We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algorithmic information theory (AIT), in general, and algorithmic randomness and Kolmogorov complexity (KC), in particular. The processing and recognition tasks addressed include data discrimination and multilayer open set data categorization, change detection, data aggregation, clustering and data segmentation, data selection and link analysis, data cleaning and data revision, and prediction and identification of critical states. The unifying theme throughout the paper is that of “compression entails comprehension”, which is realized using the interrelated concepts of randomness vs. regularity and Kolmogorov complexity. The constructive and all encompassing active learning (AL) methodology, which mediates and supports the above theme, is context-driven and takes advantage of statistical learning, in general, and semi-supervised learning and transduction, in particular. Active learning employs explore and exploit actions characteristic of closed-loop control for evidence accumulation in order to revise its prediction models and to reduce uncertainty. The set-based similarity scores, driven by algorithmic randomness and Kolmogorov complexity, employ strangeness / typicality and p-values. We propose the application of the IIM framework to critical states prediction for complex physical systems;in particular, the prediction of cyclone genesis and intensification.
