This study aims to evaluate the biosafety awareness of laboratory staff working on pathogens detection in seven Centers for Disease Control and Prevention and support these staff's biosafety management and trainin...This study aims to evaluate the biosafety awareness of laboratory staff working on pathogens detection in seven Centers for Disease Control and Prevention and support these staff's biosafety management and training.A total of 208 laboratory staff from seven provincial CDCs were chosen by stratified random sampling to undergo a self-administered questionnaire survey.The collected data were analyzed using SPSS 22.0.The overall average biosafety awareness of the CDC laboratory staff involved in pathogen detection was 82.5 points.The average biosafety awareness score was the highest in health monitoring(92.63 points)and the lowest in risk assessment and control(41.6 points).Among the seven provincial CDCs,the Guizhou Provincial Center for Disease Control and Prevention laboratory staff showed the lowest average biosafety awareness score(74.3 points).The team who worked for 5–14 years were more likely to score above the passing score(≥88 points;corrected OR 0.400,95%CI:0.168–0.951)than the staff with less than five years of work experience.In addition,the mid-level and junior-level staff,as well as the lower position ones were more likely to score below the passing score(<88 points;junior level and lower:corrected OR 3.473,95%CI:1.028–11.737;mid-level:corrected OR 2.797,95%CI:1.027–7.618)compared with the senior-level staff.Among the surveyed team,66.3%identified a lack of specific funds related to work,61.5%identified a lack of designated personnel for the laboratory biosafety management.The biosafety awareness in CDC laboratory staff involved in pathogen detection is low,especially regarding risk assessment and control.The biosafety awareness in Guizhou and Guangxi Provincial CDC laboratory staff is also low.Laboratory funding,job title,and years of experience in a laboratory influence biosafety awareness in CDC laboratory staff.The biosafety knowledge,education,and training of CDC laboratory staff involved in pathogen detection need to improve by paying attention to the content and coverage of biosafety training,exploring new training modalities,and increasing funding for activities related to biosafety in CDC.展开更多
This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed a...This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.展开更多
As an important component of worldwide injury prevention,China has made great contribution to the development.China is serving as a model for other countries in the world,especially developing countries,and China’s e...As an important component of worldwide injury prevention,China has made great contribution to the development.China is serving as a model for other countries in the world,especially developing countries,and China’s experience has great implications for them.Besides the description of the present situation and trends of China’s injury prevention work and review for its development history,this paper has also introduced the existing issues and the current challenges,and informed the e orts of the sta in related eld to nd solutions from di erent aspects.All of them jointly boosted the development of global injury prevention.展开更多
目的通过对汕头市、区二级疾控中心的人力资源相关数据进行比较分析,了解疾控中心人力资源配置现状,为汕头市各级疾控中心人才资源配置及专业队伍建设提供参考。方法通过标准表格收集汕头市2022年12月—2023年5月1个市级和7个区(县)疾...目的通过对汕头市、区二级疾控中心的人力资源相关数据进行比较分析,了解疾控中心人力资源配置现状,为汕头市各级疾控中心人才资源配置及专业队伍建设提供参考。方法通过标准表格收集汕头市2022年12月—2023年5月1个市级和7个区(县)疾控中心的人力资源数据,采用描述性方法对疾控中心人员的性别、年龄、职称、学历学位和专业背景等情况进行分析。使用Excel和GraphP ad Prism软件进行数据统计及差异比较分析。结果汕头市、区二级疾控中心人员性别构成较均衡,男女比例分别为48.1%和51.9%,差异无统计学意义(χ^(2)=0.66,P=0.42)。市级疾控中心35岁以下人员占比较区(县)级CDC高(分别为35.5%和18.9%),二级疾控中心人员年龄构成差异有统计学意义(χ^(2)=16.68,P<0.001)。市级疾控中心人员学历以本科、硕士为主,占77.6%;但区(县)疾控中心职工学历严重失衡,以本科、大专为主,硕士人员比例仅为1.7%,二者学历构成差异有统计学意义(χ^(2)=87.43,P<0.001);市级疾控中心人员职称集中在中级、高级职称,占比为55.2%,无职称人员占比为10.3%;区(县)疾控中心人员职称主要集中在中、初级,占比为71.4%,无职称人员占比为21.0%,二者专业技术职称构成差异有统计学意义(χ^(2)=33.99,P<0.001);二级疾控中心职工专业背景均以卫生专业为主(77.4%),二者专业背景构成差异无统计学意义(χ^(2)=1.35,P=0.24)。结论汕头市、区二级疾控中心在编人员总量不足,其中区(县)疾控中心职工总体学历偏低,人才配置不平衡,高层次人才总体缺乏;当地政府应当重视疾控人才队伍建设,提高已有专业人员的专业水平,积极引进高层次人才,切实做好本市各级疾控中心的人力资源规划。展开更多
基金supported by grants from the Establishment of Technical Simulation Training Platform for High-Level Biosafety Laboratory(A3705011905-23-2).
文摘This study aims to evaluate the biosafety awareness of laboratory staff working on pathogens detection in seven Centers for Disease Control and Prevention and support these staff's biosafety management and training.A total of 208 laboratory staff from seven provincial CDCs were chosen by stratified random sampling to undergo a self-administered questionnaire survey.The collected data were analyzed using SPSS 22.0.The overall average biosafety awareness of the CDC laboratory staff involved in pathogen detection was 82.5 points.The average biosafety awareness score was the highest in health monitoring(92.63 points)and the lowest in risk assessment and control(41.6 points).Among the seven provincial CDCs,the Guizhou Provincial Center for Disease Control and Prevention laboratory staff showed the lowest average biosafety awareness score(74.3 points).The team who worked for 5–14 years were more likely to score above the passing score(≥88 points;corrected OR 0.400,95%CI:0.168–0.951)than the staff with less than five years of work experience.In addition,the mid-level and junior-level staff,as well as the lower position ones were more likely to score below the passing score(<88 points;junior level and lower:corrected OR 3.473,95%CI:1.028–11.737;mid-level:corrected OR 2.797,95%CI:1.027–7.618)compared with the senior-level staff.Among the surveyed team,66.3%identified a lack of specific funds related to work,61.5%identified a lack of designated personnel for the laboratory biosafety management.The biosafety awareness in CDC laboratory staff involved in pathogen detection is low,especially regarding risk assessment and control.The biosafety awareness in Guizhou and Guangxi Provincial CDC laboratory staff is also low.Laboratory funding,job title,and years of experience in a laboratory influence biosafety awareness in CDC laboratory staff.The biosafety knowledge,education,and training of CDC laboratory staff involved in pathogen detection need to improve by paying attention to the content and coverage of biosafety training,exploring new training modalities,and increasing funding for activities related to biosafety in CDC.
文摘This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.
文摘As an important component of worldwide injury prevention,China has made great contribution to the development.China is serving as a model for other countries in the world,especially developing countries,and China’s experience has great implications for them.Besides the description of the present situation and trends of China’s injury prevention work and review for its development history,this paper has also introduced the existing issues and the current challenges,and informed the e orts of the sta in related eld to nd solutions from di erent aspects.All of them jointly boosted the development of global injury prevention.
文摘目的通过对汕头市、区二级疾控中心的人力资源相关数据进行比较分析,了解疾控中心人力资源配置现状,为汕头市各级疾控中心人才资源配置及专业队伍建设提供参考。方法通过标准表格收集汕头市2022年12月—2023年5月1个市级和7个区(县)疾控中心的人力资源数据,采用描述性方法对疾控中心人员的性别、年龄、职称、学历学位和专业背景等情况进行分析。使用Excel和GraphP ad Prism软件进行数据统计及差异比较分析。结果汕头市、区二级疾控中心人员性别构成较均衡,男女比例分别为48.1%和51.9%,差异无统计学意义(χ^(2)=0.66,P=0.42)。市级疾控中心35岁以下人员占比较区(县)级CDC高(分别为35.5%和18.9%),二级疾控中心人员年龄构成差异有统计学意义(χ^(2)=16.68,P<0.001)。市级疾控中心人员学历以本科、硕士为主,占77.6%;但区(县)疾控中心职工学历严重失衡,以本科、大专为主,硕士人员比例仅为1.7%,二者学历构成差异有统计学意义(χ^(2)=87.43,P<0.001);市级疾控中心人员职称集中在中级、高级职称,占比为55.2%,无职称人员占比为10.3%;区(县)疾控中心人员职称主要集中在中、初级,占比为71.4%,无职称人员占比为21.0%,二者专业技术职称构成差异有统计学意义(χ^(2)=33.99,P<0.001);二级疾控中心职工专业背景均以卫生专业为主(77.4%),二者专业背景构成差异无统计学意义(χ^(2)=1.35,P=0.24)。结论汕头市、区二级疾控中心在编人员总量不足,其中区(县)疾控中心职工总体学历偏低,人才配置不平衡,高层次人才总体缺乏;当地政府应当重视疾控人才队伍建设,提高已有专业人员的专业水平,积极引进高层次人才,切实做好本市各级疾控中心的人力资源规划。
