医院信息化平台住院陪护人员管理功能的开发与应用

目的 探讨医院信息系统中增加住院陪护管理功能的应用效果。方法 基于互联网医院、智慧医院等信息系统，开发信息化住院陪护管理功能，包括流行病学史调查、免费核酸申请、电子陪护证办理、体温监测登记及上报和统计查询。该功能与医院智慧护理链接后全院应用。比较功能应用前和应用后的遵医嘱一患一陪达标率、有效陪护证达标率、体温监测并登记日上报达标率和陪护证使用追溯率，评价护士和管理者疫情防控管理的人均耗时以及对该管理功能的满意度。结果 应用信息化陪护管理功能后，一患一陪达标率、有效陪护证达标率、体温监测并登记日上报达标率和陪护证使用追溯率显著高于应用前（均P＜0.05）；护士陪护管理人均耗时从（554.13±30.77）s降至（311.67±21.54）s（P＜0.05）；护士和管理者对该信息化陪护管理功能的满意度显著提高（均P＜0.05）。结论 信息化住院陪护管理功能的应用有效提升了疫情期间陪护的管理质量和管理效率，提高了一线护士和管理者的满意度。     

护士"互联网+护理服务"参与行为意愿量表的编制及信效度检验

目的编制护士"互联网+护理服务"参与行为意愿量表,并进行信效度检验。方法以计划行为理论为框架,采用文献回顾、半结构式访谈法形成量表的条目池;通过专家咨询、项目分析对量表条目进行筛选,初步形成护士"互联网+护理服务"参与行为意愿量表。采用方便抽样法,于2020年10—12月选取江苏省6所医院(2所三级医院、2所二级医院、2所一级医院)的825名护士进行正式调查,检验问卷的信效度。结果护士"互联网+护理服务"参与行为意愿量表包括3个分量表(参与行为态度、主观行为规范、感知行为控制)、8个维度、17个条目。总量表的Cronbach'sα系数为0.927、重测信度系数为0.805。护士"互联网+护理服务"参与行为意愿与参与行为的相关系数为0.743(P<0.01)。因子分析提取出8个公因子,累积方差贡献率为72.33%。结论护士"互联网+护理服务"参与行为意愿量表具有较好的信效度,可用于护士"互联网+护理服务"参与行为意愿的评估。     

Pentobarbital overdose: A case of contract killing

Suicides by pentobarbital overdose have increased since about 2012, which appear to be influenced by technical information on active euthanasia that has spread over the Internet. We encountered a pentobarbital poisoning case of a patient with amyotrophic lateral sclerosis. A caregiver found the patient unconscious immediately after two visitors left the room. The patient was immediately transferred to the emergency hospital but eventually declared dead. A fatal concentration of pentobarbital was detected in peripheral blood samples collected in the emergency hospital and during autopsy (53.8 μg/mL and 29.4 μg/mL, respectively). Because the ratios of pentobarbital concentrations between the gastric contents and peripheral blood were 35 and 29 in the hospital and autopsy samples, respectively, it is likely that pentobarbital was administered via the gastrostomy tube. The patient had contacted the visitors through social media. Although the patient had requested the doctor perform active euthanasia and expressed a desire to end their life on social media, nobody had noticed the plan to commit suicide.     

Constructing queer mother‐knowledge and negotiating medical authority in online lesbian pregnancy journals

Medical interactions around reproduction are increasingly extending beyond the physician's office and onto the Internet, where negotiation with medical authority occurs in complex and dynamic ways. Recently, scholars have noted the Internet's potential for creating spaces where women can dialogue with and reconstruct medical authority, yet this growing body of work is overwhelming heteronormative. This paper thus interrogates how lesbian women use the Internet to challenge, deploy, and rework medical authority around reproduction while navigating the transition to parenthood. I draw from 17 online journals authored by lesbian couples during the conception, pregnancy, and birth of their first child, each spanning between 18 months and 2 years, in order to understand how the transition process unfolds over time. I argue that lesbian couples engage with medical authority when seeking affirmation and normalisation yet discard and publicly reject the heteronormative assumptions that accompany reproductive medicine. Further, they chart a new process that I term ‘constructing queer mother‐knowledge’, in which they critique and balance knowledges from institutionalised medicine, their own bodies, and their queer communities. With this new concept, I complicate understandings of lesbian mothers‐to‐be and their interactions with medical authority as they build subversive families.     

Re-evaluation of websites from burn centers in Austria,Germany and Switzerland

Introduction

Websites serve as information and communication platforms; hence, they are important tools for the self-promotion of hospitals. In 2010, Selig et al. evaluated the online presence of burn centers in Germany, Austria, and Switzerland based on 37 quality criteria. This study aimed to re-evaluate these websites to assess their development over the past 6.5 years.

Impact of a web-based personally tailored physical activity intervention on depression,anxiety, stress and quality of life: Secondary outcomes from a randomized controlled trial

BackgroundFew studies have investigated the impact of web-based physical activity interventions on mental health outcomes. Therefore, this study examined the impact of a web-based personally tailored physical activity intervention on depression, anxiety, stress and quality of life.Methods501 participants were randomised into either a control group or a pooled intervention condition who received a 3-month web-based personally tailored physical activity intervention. Previously, this intervention has demonstrated to improve self-reported physical activity, but not device-measured physical activity. At baseline, 3- and 9-months, depression, anxiety and stress were assessed using the DASS21, and quality of life was assessed using the SF-12V2. General linear mixed models examined differences between groups over time.ResultsMost participants (>80%) reported normal levels of depression, anxiety or stress. Relative to baseline levels, significant reductions of depression, anxiety, stress and the SF12 mental health component were observed in the pooled intervention group at 3 and 9 months. Relative to the control group, significant reductions were observed in the pooled intervention group for depression and stress (3-months only) and anxiety (3- and 9-months), but not quality of life.ConclusionA web-based physical activity intervention can result in positive mental health outcomes, even in the absence of device-measured physical activity improvements. However, these findings need to be confirmed in future studies.Trial registration numberACTRN12615000057583.     

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).