后丹丹
芜湖市第四人民医院 山东芜湖 241000
【摘要】目的 探讨冲动行为干预在重性精神病患者护理管理中的效果评价。方法 纳入本院2019年02月-2020年07月的72例重性精神病患者,随机将其分为两组,分别予以冲动行为干预(观察组,n=36)和常规干预(参照组,n=36),观察干预效果。结果 经对比,观察组的NOSIE评分优于参照组;两组的服药依从性分别为69.44%、94.44%,差异明显(P<0.05)。结论 对于重性精神病患者来说,冲动行为干预不管是对于改善患者的NOSIE评分还是提高患者的用药依从性都是非常有利的。
【关键词】冲动行为干预;重性精神病;护理管理
当前,重性精神病已经成为对人类身体健康造成严重影响的重要社会问题以及公共卫生问题。尤其是对于大部分患者,其一般会伴随躁狂症、抑郁症、双相障碍以及精神分裂症等,用药依从性以及自我认知都相对较差。最近几年,非典型抗精神病药物在临床中获得了一定应用,其可以有效改善患者预后,但是其对护理干预也提出了较高要求[1]。冲动行为干预是对患者冲动行为进行保护和控制的重要方式,主要包括心理指导、行动约束以及药物镇静等。本文主要是探讨冲动行为干预在重性精神病患者护理管理中的效果评价,见下文。
1 资料和方法
1.1.一般资料
纳入本院2019年02月-2020年07月的72例重性精神病患者,随机将其分为两组,分别36例。参照组男女比例为20:16,平均年龄为(58.16±1.36)岁,观察组男女比例为21:15,平均年龄为(58.49±1.69)岁。两组一般资料无统计学意义(P>0.05)。将存在脑外疾病患者以及存在脑出血病史的患者排除在外。所有患者以及患者家属均知晓并且同意本次实验研究,其临床资料完整。
1.2方法
1.2.1参照组
该组实行常规干预。主要是监督并指导患者按时用药,并为其讲述合理用药的重要性。
1.2.2观察组。
该组实行冲动行为干预。①创建冲动行为干预小组。小组成员包括责任护士、护士长等;按照患者的现实病情制定针对性的护理干预,护理过程中要定期对护理人员进行培训,与此同时还要评估患者的各项指标,按照评估结果对护理方案进行合理调整。②护理前后要充分了解患者的社会背景以及家庭情况,并和患者及患者家属保持沟通,创建出和谐的护患关系;营造出良好的住院环境,消除患者的紧张、陌生感。③为了保证患者安全,在对患者实行快速镇静以及治疗的过程中要按照患者的暴力以及冲动行为为患者提供劳拉西泮和氯硝西泮等药物,同时记录患者的冲动以及暴力行为,以便规划长期治疗方案。④冲动行为干预。充分了解患者病情以及基本资料,同时强化患者的自伤、冲动行为、妄想以及幻觉等防范;强化病室安全管理,做好对忌品以及危险品的检查工作,如果病房中存在危险物品,则要进行及时清除;强化巡视,观察患者的病情同时按照其现实病情对患者实行对症处理,以温和的态度和患者保持沟通和交流。
1.3观察指标
①通过NOSIE来对患者的认知功能以及社会能力进行评估,并记录。
②观察患者的用药依从性情况。
1.4统计学分析
以软件SPSS20.0分析统计值,计量和计数资料分别以(±s)、百分号描述,开展t和x2检验;组间值P<0.05时存在统计学意义。
2 结果
2.1对比两组的NOSIE评分
在观察组中,其各项评分均优于参照组,差异显著(P<0.05)。详见表1。
表1 对比两组的NOSIE评分[n(±s)]
![](data:image/png;base64,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)
2.2两组用药依从性观察
和参照组的用药依从性相比较,观察组相对较高(P<0.05)。详见表2。
表2 两组用药依从性观察[n(%)]
![](data:image/png;base64,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)
3 讨论
社会经济的迅猛发展以及社会竞争的日益激烈,再加上受到外界环境因素的影响,重性精神病的发病率越来越高,其会在一定程度上对患者的生活产生影响,严重的甚至还会造成精神残疾,会对患者预后产生不利影响[2]。
临床在对重性精神病的治疗中,最为常见的治疗方式就是抗精神药物治疗,但是患者在治疗过程中,其很容易产生各种冲动行为,因此强化其行为干预是非常必要的。通过对患者实行健康宣教,和患者保持交流,了解其心理状态,能够使其紧张担忧情绪得到有效缓解,使患者保持乐观的心态面对疾病,同时对于提高患者的治疗信心以及强化患者以及患者家属对疾病的认识和了解也有着非常重要的影响[3]。对非病理性原因实行心理指导,需要强化社会以及家庭支持,并充分尊重患者,最大程度地满足患者的各项合理需求。依从性指的是患者对于科研实验措施以及医疗护理的服从以及接受程度,用药依从性会对患者疾病转归以及预后产生直接性的影响。但是对于大部分患者来说,其自制力相对较差,再加上文化程度相对较低,因此其用药依从性也相对较低。为了能够进一步提高患者的用药依从性,需要督促并鼓励患者按时用药,同时对其实行冲动行为干预,如果有必要的话则要对其行动进行约束[4]。本次研究结果显示,经对比,观察组的NOSIE评分优于参照组;两组的服药依从性分别为69.44%、94.44%,差异明显(P<0.05)。证明对于重性精神病患者来说,冲动行为干预不管是对于改善患者的NOSIE评分还是提高患者的用药依从性都是非常有利的。
综上所述,通过对患者实行冲动行为干预,其可以进一步提高重性精神病患者的依从性,同时对于改善患者的NOSIE评分也有着非常显著的作用。
【参考文献】
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[2]聂迎春, 江慎辉, 徐玉琴. 非暴力危机干预技巧在精神科护理中的应用[J]. 临床医药实践, 2018, 27(11):874-876.
[3]郑延华, 汪丽宏. 精神障碍患者冲动行为的护理分析[J]. 中国继续医学教育, 2018, v.10(7):174-175.
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