互联网形势下医院离退休干部管理工作探索

当前我国科技技术进一步提升,国民生活水平也在不断地进步,对于医院离退休干部管理服务的要求也越来越高。对于医院的工作来说,处理好医院的退离休老干部是一项十分重要的工作,所以医院工作管理人员需要提高对于离退休老干部管理工作的重视。而在传统意义下医院离退休干部的管理服务,存在着较多的问题,而当前在互联网的形势下却能够实现更加具有科学化、合理化的服务。通过创新服务理念,对于管理方式进行改革,可以有效地改善当前对于医院离退休干部的管理服务水平,青年团员可以自行组成相应的志愿者,加强对于退休老人的关心,了解老干部在生活以及学习中出现的问题,并且采取有效的措施进行解决。本文通过以阐述我国当前离退休干部管理工作的现状作为切入点,进行分析探究,进一步提出互联网模式下提高我国医院离退休干部管理的对应措施。     

Psychometric properties of the Repetitive Behavior Scale-Revised for individuals with autism spectrum disorder in Japan

Restricted and repetitive behaviors (RRBs) constitute a core symptom of autism spectrum disorder (ASD). The Repetitive Behavior Scale-Revised (RBS-R) is a widely used questionnaire administered by parents or caregivers to assess RRBs in individuals with ASD. This study evaluated the psychometric properties of the RBS-R Japanese Version (RBS-R-J). The ASD and non-ASD groups comprised 274 and 36 participants, respectively. We examined corrected item-total correlation, Cronbach's alpha, and RBS-R-J scores of different diagnostic groups, as well as correlations between RBS-R-J scores and intelligence quotient (IQ), autistic symptoms, adaptive/maladaptive functioning, aberrant behaviors, and sensory processing. All items showed moderate corrected item-total correlations. Cronbach's alpha coefficient was .93. We found significant differences in the mean RBS-R-J scores of the low-functioning ASD group and the intellectual disabilities group, and of low-functioning and high-functioning ASD groups. RBS-R-J scores negatively correlated with IQ and scores on the Sensory Profile (Japanese version) and Adaptive Behavior Composite of the Maladaptive Behavior Index of the Vineland Adaptive Behavior Scales-Second Edition (VABS-II; Japanese version), but positively correlated with scores on the peak and current symptoms subscales of the Pervasive Developmental Disorders Autism Society Japan Rating Scale, the VABS-II, and the Aberrant Behavior Checklist-Community (Japanese version). From these results, we conclude that RBS-R-J showed good reliability, diagnostic validity, and convergent validity, indicating that it is a reliable, valid instrument for use among ASD individuals in clinical and research settings.     

Racial matching and adolescent self-disclosure of substance use and mental health symptoms

Obtaining accurate assessment data from adolescents in treatment aids clinical decision making and facilitates more accurate outcome evaluations. However, findings could be biased due to underreported substance use and mental health symptoms. This article compares self-reports of youth in non-White matched client–assessor dyads and those in nonmatched dyads. There were no differences on self-reported substance use, but matched youth reported significantly fewer attention deficit/hyperactivity disorder symptoms versus the comparison group. One possible reason for these findings is the effect of in-group stereotype threat. Future studies should examine the potential effect that in-group stereotyping and perceived racism have on the therapeutic relationship.     

行为-结构化-关系干预模式对短期住院孤独症谱系障碍儿童的疗效分析

目的 探讨以行为-结构化-关系(BSR)干预模式对短期住院孤独症谱系障碍(ASD)儿童的干预疗效,为改善ASD儿童的预后提供参考依据。方法 选取2015年12月－2016年12月确诊为ASD的2~6岁儿童141例,随机分为治疗组和对照组。治疗组ASD儿童均接受BSR模式的短期课程训练,训练课程包括个别辅导、游戏课、运动课、音乐课等,每天训练时间6 h,持续1个月;对照组ASD儿童处于干预等待,接受随访观察和评估。所有ASD儿童干预前、后均接受儿童心理教育评估(第三版)(PEP-3)来进行各方面能力变化的评估。结果 治疗组、对照组干预前各副测验的原积分差异无统计学意义(P>0.05)。两组ASD儿童干预后认知、语言理解、模仿、情感表达、社会互动、行为特征-非语言、适应行为的原积分比较,差异均有统计学意义(t=2.41、2.02、4.14、3.69、4.42、2.69、2.96,P<0.05);但小肌肉、大肌肉、自理的原积分比较,差异无统计学意义(t=-1.13、-1.05、-0.84,P>0.05)。结论 BSR干预模式能够有效改善短期住院ASD儿童的预后,可推广用于儿童ASD的治疗。     

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).