肿瘤医院护理人员安全文化态度的现状调查

目的：了解肿瘤医院护理人员安全文化态度现状,为医院加强安全护理,制定安全干预措施提供理论依据。方法采用修订的中文版安全态度调查问卷对某三级甲等肿瘤医院331名护理人员的安全文化态度进行问卷调查。结果护理人员安全态度平均得分为（125．52±17．96）分,不同性别、职称、学历及婚姻状态的护理人员的安全文化态度总分差异有统计学意义（P＜0．05）。护理人员安全态度各维度得分与总分均呈正相关（r值分别为0．811,0．865,0．850,0．864,0．863,0．341;P＜0．05）。经多元线性回归分析,性别、婚姻、学历、职称是安全文化态度的影响因素（P＜0．05）。结论护理管理者应关注中级职称的护理人员的工作满意度,加强与本科及以上学历护理人员的沟通与引导,帮助已婚护理人员处理好多重角色冲突,以提高护理人员安全文化态度的积极性,促进安全护理行为的养成,保障患者的安全。     

From the Cover: Satellites can reveal global extent of forced labor in the world’s fishing fleet

While forced labor in the world’s fishing fleet has been widely documented, its extent remains unknown. No methods previously existed for remotely identifying individual fishing vessels potentially engaged in these abuses on a global scale. By combining expertise from human rights practitioners and satellite vessel monitoring data, we show that vessels reported to use forced labor behave in systematically different ways from other vessels. We exploit this insight by using machine learning to identify high-risk vessels from among 16,000 industrial longliner, squid jigger, and trawler fishing vessels. Our model reveals that between 14% and 26% of vessels were high-risk, and also reveals patterns of where these vessels fished and which ports they visited. Between 57,000 and 100,000 individuals worked on these vessels, many of whom may have been forced labor victims. This information provides unprecedented opportunities for novel interventions to combat this humanitarian tragedy. More broadly, this research demonstrates a proof of concept for using remote sensing to detect forced labor abuses.

Forced labor in fisheries, a type of modern slavery, is increasingly recognized as a human rights crisis. The International Labor Organization (ILO) defines forced labor as “all work or service which is exacted from any person under the menace of any penalty and for which the said person has not offered himself voluntarily” (1). The ILO provides a framework of 11 forced labor risk indicators (2) that have all been documented within the fisheries sector, including indicators representative of debt-bonded labor, as well as indicators representative of servitude or slave labor such as abusive working and living conditions. In 2015, reports emerged on forced labor in Thai fisheries (3) and the role of forced labor in producing seafood imported to the United States (4). More recent reports have described the global nature of the problem (5), and there has been a call to integrate social responsibility into ocean science (6). Despite widespread condemnation and ambitious commitments, forced labor remains poorly understood in the fisheries sector. Here we show that recently available high-frequency vessel monitoring of the global industrial fishing fleet can shed new light on forced labor at a much finer resolution. We combine expertise from on-the-ground human rights practitioners and satellite vessel monitoring data for over 16,000 industrial fishing vessels to estimate 1) the number of high-risk vessels and the number of crew who may be victims working on those vessels, 2) where these vessels fish, and 3) what ports these vessels visit. This information can inform new market, policy, and enforcement interventions to combat forced labor in global fisheries. This research more generally demonstrates how remote sensing can detect forced labor abuses by observing dynamic behavior.Current estimates of forced labor in fisheries are coarse and are based on country-level statistics. Using country-level household surveys, the ILO estimated that 16 million people were victims of forced labor in 2016, with 11% of these in agriculture, forestry, or fisheries (7). The Global Slavery Index reports that the seven countries with highest slavery risk in 2018 generated 39% of global fisheries catch (3, 8), and Tickler et al. found that the United States has slavery risks of 0.2 kg per metric ton for domestic seafood and 3.1 kg per metric ton for imported seafood (9). While these studies are important for broadly understanding which countries have risk, current methods are unable to detect this problem at the level of individual fishing vessels, which will be essential for targeted interventions.We empirically examine whether vessels reported to exhibit any of the ILO indicators of forced labor behave in ways that are systematically different from other vessels, and then exploit this information using machine learning to discriminate between vessels that use forced labor from those that do not. We do so by measuring a suite of features that can be observed using satellite Automatic Identification System (AIS) vessel monitoring data made available by Global Fishing Watch (GFW) (10). There may be many behavioral correlates with forced labor that could help to differentiate between high-risk and low-risk vessels. To determine which model features to include, we first conducted a literature review of investigative journalism reports and looked for instances of forced labor case accounts that detailed specific behaviors that could be observed using vessel monitoring data. We next conducted informal phone interviews with experts from several nongovernmental organizations (NGOs) working in this field, during which we asked interviewees what observable vessel behaviors they would look for if they wanted to identify suspicious activity. The machine-learning approach we use does not assume that vessels behave in any particular way; rather, it merely uses the features identified by literature review and expert insight to exploit any observed empirical differences between vessels that use forced labor and other vessels. NGO experts and investigative journalism suggest that gaps in AIS transmission, port avoidance, transshipment, and extended time at sea may indicate the presence of forced labor (11). Certain features, like information on catch and the species being targeted, could also be helpful in discriminating between high- and low-risk behavior by providing more context on the fishing taking place. However, these data are not currently available at the vessel level on a global scale. Data on recruitment practices and vessel ownership and information on from where the crew originates could also be helpful, but, again, these data are not widely available. We arrived at a list of 27 vessel behavior and characteristic features for which we have globally available data at the vessel level (SI Appendix, Table S1, and SI Appendix).To build a predictive model for identifying high-risk vessels, we developed a training dataset that includes the behavior and characteristics of known forced-labor vessels, as well as the behavior and characteristics of other vessels. We compiled a comprehensive database of vessels that were reported to display one or more of the ILO forced labor indicators (2); these vessels are labeled as “positives.” We do not, however, know which vessels do not use forced labor (“negatives”). Rather, any vessel that we do not label as positive is “unlabeled,” and may in fact be a positive vessel that has not yet been identified or may truly be a negative vessel. This is an example of “positive-unlabeled (PU)” learning, a less straightforward problem than traditional supervised machine learning (12). We use PU learning to predict whether or not 16,261 longliner, trawler, and squid jigger fishing vessels were high-risk during each year they operated between 2012 and 2018 (“vessel-years”). We focus on this subset of vessels because they broadcasted sufficient and reliable AIS positions and because these are the only fishing gear types with documented cases of forced labor aboard vessels that broadcasted sufficient AIS data. These vessels represent 33% of the total time at sea spent by all fishing vessels operating in this time period tracked by GFW. Our PU approach leverages information from all positively labeled vessels (n = 22 unique vessels across 22 vessel-years using our baseline model assumption), but places less emphasis on unlabeled vessels given their uncertain nature (n = 16,257 unique vessels across 66,314 vessel-years using our baseline model assumption).     

内蒙古地区听障儿童家庭教育情况调查研究

目的：调查分析听障儿童家庭运用听觉口语法实施家庭教育的现状和困境,为听障儿童家庭康复训练提供支持和帮助。方法自编调查问卷,对内蒙古地区100例听障儿童家长进行问卷调查,采用SPSS 19.0对数据进行统计分析。结果听障儿童家庭对听觉口语法的教学理念和方法掌握程度较差,对家庭教育康复训练的重视程度不够,作业反馈少且缺乏与教师的沟通。结论听障儿童家长迫切需要实施听觉口语法家庭康复训练的支持和帮助,康复机构教师应加强与家长的沟通、协调与指导,使听觉口语法在听障儿童家庭教育中发挥更大的作用。     

B-Type Natriuretic Peptide Levels and Mortality in Patients With and Without Heart Failure

Background

Circulating B-type natriuretic peptide (BNP) concentrations strongly predict mortality in patients with heart failure (HF). Both cardiac and extracardiac stimuli influence BNP levels, suggesting that BNP might have similar prognostic value in patients without HF.

67名正常青年人上前牙区唇、腭侧骨厚度测量分析

目的:对汉族正常青年人上前牙区不同位点的唇、腭侧牙槽骨厚度进行测量,为术前评估、治疗方案制定及预后评估提供参考。方法:通过锥形束CT(CBCT)对67名符合条件的汉族青年志愿者进行上颌骨扫描。三维重建后,对前牙区唇、腭侧骨厚度进行测量。利用SPSS17.0软件包对测量数据进行配对t检验、独立样本t检验、方差分析及χ2检验。结果:1唇侧骨板除所有前牙L5处及上颌尖牙L1处厚度均数>1.00 mm外,其余测量位点唇侧骨板厚度均数均<1.00mm,而腭侧骨板厚度均数均>1.00 mm;2唇侧骨板厚度均小于腭侧骨板厚度(P<0.001);33种前牙唇侧骨板厚度在参考线L3与L4处厚度最小(P<0.001),腭侧骨板厚度自L1至L5逐渐增大(P<0.05);4仅在上颌尖牙L2处及所有前牙L5处唇侧骨板厚度<1.00 mm的频率<50%,男性上颌侧切牙L3、L4处及上颌尖牙L4处唇侧骨板缺如所占频率>50%;5男性切牙区唇侧骨板凹陷角度较尖牙小(P<0.05),唇侧骨板最凹点与根尖点之间的距离在男性上颌中切牙最大(P<0.05),女性牙位间无显著差异。结论:正常青年人上前牙牙槽骨骨板菲薄甚至缺如,且唇、腭侧骨厚度和形态存在差异。     

产前超声诊断中晚孕期胎儿心脏畸形48例应用分析

目的探讨产前超声在胎儿心脏畸形诊断中的应用价值。方法回顾性分析2012年9月至2013年9月于陕西中医学院第二附属医院产前超声筛查中诊断的心脏畸形胎儿48例的临床资料及声像图表现,并分析总结3例漏诊的原因。结果 37例胎儿心脏畸形于中孕期检出,占总数的77.1%;晚孕期检出11例,占22.9%,其中1例室间隔缺损胎儿于孕晚期复查时缺损完全愈合,漏诊3例均于出生后经超声心动图确诊。结论产前常规超声检查可提高胎儿心脏畸形的检出率,并减少漏诊、误诊的发生。     

首发精神分裂症患者及其一级亲属返回抑制对照研究

目的：探讨首发精神分裂症患者及其一级亲属注意返回抑制特点。方法将31例首发精神分裂症患者设为患者组,29名健康一级亲属设为亲属组,30名健康志愿者设为对照组。分别对3组被试进行注意返回抑制测试。结果3组不同线索开始呈现到靶子的时间间隔水平靶子同侧反应时间均显著长于异侧反应时间（P＜0．01）,均存在返回抑制;3组所有线索开始呈现到靶子的时间间隔水平靶子同侧与异侧反应时间依次为：患者组＞亲属组＞对照组。结论首发精神分裂症患者及其一级亲属存在返回抑制功能障碍,这可能是精神分裂症的遗传易感性指标。