目的探讨多模式分层心理干预结合阶段化情景模拟教育在初产妇中的应用价值。方法选取2020年7月至2022年10月于江苏省盐城市第一人民医院分娩的182名初产妇为研究对象,采用随机数字表法将其分别纳入对照组(91名)和观察组(91名)。对照组...目的探讨多模式分层心理干预结合阶段化情景模拟教育在初产妇中的应用价值。方法选取2020年7月至2022年10月于江苏省盐城市第一人民医院分娩的182名初产妇为研究对象,采用随机数字表法将其分别纳入对照组(91名)和观察组(91名)。对照组接受常规健康教育和孕期护理,观察组在对照组的基础上给予多模式分层心理干预结合阶段化情景模拟教育。比较2组的分娩结局等指标。结果分娩前及产后24 h,2组的分娩恐惧测评量表、焦虑和抑郁自评量表评分均显著低于同组入院时,结果期望评分和分娩自我效能期望评分均显著高于同组入院时(P均<0.05),且观察组均显著优于同期对照组(P均<0.05)。观察组产妇的自然分娩率高于对照组,产后2 h出血量明显少于对照组,总产程明显短于对照组,新生儿1 min Apgar评分明显高于对照组(P均<0.05)。结论多模式分层心理干预结合阶段化情景模拟教育可改善初产妇对分娩的焦虑、抑郁和恐惧情绪,提高其自我分娩效能,明显改善分娩结局。展开更多
In this study, a Multi-Layer BP neural network(MLBP) with dynamic thresholds is employed to build a classifier model.As to the design of the neural network structure, theoretical guidance and plentiful experiments are...In this study, a Multi-Layer BP neural network(MLBP) with dynamic thresholds is employed to build a classifier model.As to the design of the neural network structure, theoretical guidance and plentiful experiments are combined to optimize the hidden layers' parameters which include the number of hidden layers and their node numbers.The classifier with dynamic thresholds is used to standardize the output for the first time, and it improves the robustness of the model to a high level.Finally, the classifier is applied to forecast box office revenue of a movie before its theatrical release.The comparison results with the MLP method show that the MLBP classifier model achieves more satisfactory results, and it is more reliable and effective to solve the problem.展开更多
文摘目的探讨多模式分层心理干预结合阶段化情景模拟教育在初产妇中的应用价值。方法选取2020年7月至2022年10月于江苏省盐城市第一人民医院分娩的182名初产妇为研究对象,采用随机数字表法将其分别纳入对照组(91名)和观察组(91名)。对照组接受常规健康教育和孕期护理,观察组在对照组的基础上给予多模式分层心理干预结合阶段化情景模拟教育。比较2组的分娩结局等指标。结果分娩前及产后24 h,2组的分娩恐惧测评量表、焦虑和抑郁自评量表评分均显著低于同组入院时,结果期望评分和分娩自我效能期望评分均显著高于同组入院时(P均<0.05),且观察组均显著优于同期对照组(P均<0.05)。观察组产妇的自然分娩率高于对照组,产后2 h出血量明显少于对照组,总产程明显短于对照组,新生儿1 min Apgar评分明显高于对照组(P均<0.05)。结论多模式分层心理干预结合阶段化情景模拟教育可改善初产妇对分娩的焦虑、抑郁和恐惧情绪,提高其自我分娩效能,明显改善分娩结局。
基金Supported by National Natural Science Foundation of China (No. 60573172)
文摘In this study, a Multi-Layer BP neural network(MLBP) with dynamic thresholds is employed to build a classifier model.As to the design of the neural network structure, theoretical guidance and plentiful experiments are combined to optimize the hidden layers' parameters which include the number of hidden layers and their node numbers.The classifier with dynamic thresholds is used to standardize the output for the first time, and it improves the robustness of the model to a high level.Finally, the classifier is applied to forecast box office revenue of a movie before its theatrical release.The comparison results with the MLP method show that the MLBP classifier model achieves more satisfactory results, and it is more reliable and effective to solve the problem.
