目的修订中文版心理健康素养量表(Mental Health Literacy Scale,MHLS),并验证其信度与效度,以构建适用于中国人群心理健康素养的测量工具。方法采用便利抽样法,于2019年10月至2020年4月通过问卷星平台收集341名社区居民的有效数据。测...目的修订中文版心理健康素养量表(Mental Health Literacy Scale,MHLS),并验证其信度与效度,以构建适用于中国人群心理健康素养的测量工具。方法采用便利抽样法,于2019年10月至2020年4月通过问卷星平台收集341名社区居民的有效数据。测量工具包括中文版MHLS、简易应对方式问卷(Simplified Coping Style Questionnaire,SCSQ)、亚临床抑郁自助策略量表(Self Help Strategies for Subclinical Depression,SHS)、求助问卷(Help Seeking Questionnaire,HSQ),患者健康问卷抑郁量表-9项(Patient Health Questionnaire-9,PHQ-9)及广泛性焦虑自评量表-7项(Generalized Anxiety Disorder Scale-7,GAD-7)。采用探索性因子分析(exploratory factor analysis,EFA)和验证性因子分析(confirmatory factor analysis,CFA)检验量表信效度,通过Cronbach’sα和重测信度评估内部一致性及稳定性,从结构效度、聚合效度、区分效度和效标关联效度多维度验证测量学属性。结果EFA结果显示,中文版MHLS共提取5个公共因子,分别为疾病知识、信息寻求、对疾病的态度、对求助的态度以及污名化,累计解释率为43.297%。CFA表明,5因子模型拟合良好(χ^(2)/df=1.754),比较适配指数(comparative fit index,CFI)为0.903,增量适配指数(incremental fit index,IFI)为0.905,适配优度指数(goodness of fit index,GFI)为0.884,残差均方和平方根(root mean square error of pproximation,RMSEA)为0.047,残差均方根(root mean square residual,RMR)为0.053。总量表及各分量表的Cronbachα系数为0.701~0.877;重测信度为0.617~0.882(P<0.01)。MHLS总分与SCSQ中积极应对因子得分(r=0.213,P<0.01)、求助问卷得分(r=0.248,P<0.01)及抑郁自助策略得分(r=0.302,P<0.01)均呈显著正相关,表明该量表具有良好的准则相关效度。组间差异结果显示男性、低学历及未接受过心理健康知识培训群体的心理健康素养水平显著低于其他人群。结论中文版MHLS显示出良好的信度与效度,可作为评估个体心理健康素养的科学测量工具,有助于深入了解公众对心理健康的认知水平。同时建议多渠道科普提升男性、低学历人群的心理健康认知水平。展开更多
State Administration for Market Regulation and National Standardization Administration of China have approved the foreign language versions of the following 41 national standards in foreign language versions.
State Administration for Market Regulation and National Standardization Administration of China have approved the foreign language versions of the following 60 national standards in foreign language version.
AIM:To construct an intelligent segmentation scheme for precise localization of central serous chorioretinopathy(CSC)leakage points,thereby enabling ophthalmologists to deliver accurate laser treatment without navigat...AIM:To construct an intelligent segmentation scheme for precise localization of central serous chorioretinopathy(CSC)leakage points,thereby enabling ophthalmologists to deliver accurate laser treatment without navigational laser equipment.METHODS:A dataset with dual labels(point-level and pixel-level)was first established based on fundus fluorescein angiography(FFA)images of CSC and subsequently divided into training(102 images),validation(40 images),and test(40 images)datasets.An intelligent segmentation method was then developed,based on the You Only Look Once version 8 Pose Estimation(YOLOv8-Pose)model and segment anything model(SAM),to segment CSC leakage points.Next,the YOLOv8-Pose model was trained for 200 epochs,and the best-performing model was selected to form the optimal combination with SAM.Additionally,the classic five types of U-Net series models[i.e.,U-Net,recurrent residual U-Net(R2U-Net),attention U-Net(AttU-Net),recurrent residual attention U-Net(R2AttUNet),and nested U-Net(UNet^(++))]were initialized with three random seeds and trained for 200 epochs,resulting in a total of 15 baseline models for comparison.Finally,based on the metrics including Dice similarity coefficient(DICE),intersection over union(IoU),precision,recall,precisionrecall(PR)curve,and receiver operating characteristic(ROC)curve,the proposed method was compared with baseline models through quantitative and qualitative experiments for leakage point segmentation,thereby demonstrating its effectiveness.RESULTS:With the increase of training epochs,the mAP50-95,Recall,and precision of the YOLOv8-Pose model showed a significant increase and tended to stabilize,and it achieved a preliminary localization success rate of 90%(i.e.,36 images)for CSC leakage points in 40 test images.Using manually expert-annotated pixel-level labels as the ground truth,the proposed method achieved outcomes with a DICE of 57.13%,an IoU of 45.31%,a precision of 45.91%,a recall of 93.57%,an area under the PR curve(AUC-PR)of 0.78 and an area under the ROC curve(AUC-ROC)of 0.97,which enables more accurate segmentation of CSC leakage points.CONCLUSION:By combining the precise localization capability of the YOLOv8-Pose model with the robust and flexible segmentation ability of SAM,the proposed method not only demonstrates the effectiveness of the YOLOv8-Pose model in detecting keypoint coordinates of CSC leakage points from the perspective of application innovation but also establishes a novel approach for accurate segmentation of CSC leakage points through the“detect-then-segment”strategy,thereby providing a potential auxiliary means for the automatic and precise realtime localization of leakage points during traditional laser photocoagulation for CSC.展开更多
文摘目的修订中文版心理健康素养量表(Mental Health Literacy Scale,MHLS),并验证其信度与效度,以构建适用于中国人群心理健康素养的测量工具。方法采用便利抽样法,于2019年10月至2020年4月通过问卷星平台收集341名社区居民的有效数据。测量工具包括中文版MHLS、简易应对方式问卷(Simplified Coping Style Questionnaire,SCSQ)、亚临床抑郁自助策略量表(Self Help Strategies for Subclinical Depression,SHS)、求助问卷(Help Seeking Questionnaire,HSQ),患者健康问卷抑郁量表-9项(Patient Health Questionnaire-9,PHQ-9)及广泛性焦虑自评量表-7项(Generalized Anxiety Disorder Scale-7,GAD-7)。采用探索性因子分析(exploratory factor analysis,EFA)和验证性因子分析(confirmatory factor analysis,CFA)检验量表信效度,通过Cronbach’sα和重测信度评估内部一致性及稳定性,从结构效度、聚合效度、区分效度和效标关联效度多维度验证测量学属性。结果EFA结果显示,中文版MHLS共提取5个公共因子,分别为疾病知识、信息寻求、对疾病的态度、对求助的态度以及污名化,累计解释率为43.297%。CFA表明,5因子模型拟合良好(χ^(2)/df=1.754),比较适配指数(comparative fit index,CFI)为0.903,增量适配指数(incremental fit index,IFI)为0.905,适配优度指数(goodness of fit index,GFI)为0.884,残差均方和平方根(root mean square error of pproximation,RMSEA)为0.047,残差均方根(root mean square residual,RMR)为0.053。总量表及各分量表的Cronbachα系数为0.701~0.877;重测信度为0.617~0.882(P<0.01)。MHLS总分与SCSQ中积极应对因子得分(r=0.213,P<0.01)、求助问卷得分(r=0.248,P<0.01)及抑郁自助策略得分(r=0.302,P<0.01)均呈显著正相关,表明该量表具有良好的准则相关效度。组间差异结果显示男性、低学历及未接受过心理健康知识培训群体的心理健康素养水平显著低于其他人群。结论中文版MHLS显示出良好的信度与效度,可作为评估个体心理健康素养的科学测量工具,有助于深入了解公众对心理健康的认知水平。同时建议多渠道科普提升男性、低学历人群的心理健康认知水平。
文摘State Administration for Market Regulation and National Standardization Administration of China have approved the foreign language versions of the following 41 national standards in foreign language versions.
文摘State Administration for Market Regulation and National Standardization Administration of China have approved the foreign language versions of the following 60 national standards in foreign language version.
基金Supported by the Shenzhen Science and Technology Program(No.JCYJ20240813152704006)the National Natural Science Foundation of China(No.62401259)+2 种基金the Fundamental Research Funds for the Central Universities(No.NZ2024036)the Postdoctoral Fellowship Program of CPSF(No.GZC20242228)High Performance Computing Platform of Nanjing University of Aeronautics and Astronautics。
文摘AIM:To construct an intelligent segmentation scheme for precise localization of central serous chorioretinopathy(CSC)leakage points,thereby enabling ophthalmologists to deliver accurate laser treatment without navigational laser equipment.METHODS:A dataset with dual labels(point-level and pixel-level)was first established based on fundus fluorescein angiography(FFA)images of CSC and subsequently divided into training(102 images),validation(40 images),and test(40 images)datasets.An intelligent segmentation method was then developed,based on the You Only Look Once version 8 Pose Estimation(YOLOv8-Pose)model and segment anything model(SAM),to segment CSC leakage points.Next,the YOLOv8-Pose model was trained for 200 epochs,and the best-performing model was selected to form the optimal combination with SAM.Additionally,the classic five types of U-Net series models[i.e.,U-Net,recurrent residual U-Net(R2U-Net),attention U-Net(AttU-Net),recurrent residual attention U-Net(R2AttUNet),and nested U-Net(UNet^(++))]were initialized with three random seeds and trained for 200 epochs,resulting in a total of 15 baseline models for comparison.Finally,based on the metrics including Dice similarity coefficient(DICE),intersection over union(IoU),precision,recall,precisionrecall(PR)curve,and receiver operating characteristic(ROC)curve,the proposed method was compared with baseline models through quantitative and qualitative experiments for leakage point segmentation,thereby demonstrating its effectiveness.RESULTS:With the increase of training epochs,the mAP50-95,Recall,and precision of the YOLOv8-Pose model showed a significant increase and tended to stabilize,and it achieved a preliminary localization success rate of 90%(i.e.,36 images)for CSC leakage points in 40 test images.Using manually expert-annotated pixel-level labels as the ground truth,the proposed method achieved outcomes with a DICE of 57.13%,an IoU of 45.31%,a precision of 45.91%,a recall of 93.57%,an area under the PR curve(AUC-PR)of 0.78 and an area under the ROC curve(AUC-ROC)of 0.97,which enables more accurate segmentation of CSC leakage points.CONCLUSION:By combining the precise localization capability of the YOLOv8-Pose model with the robust and flexible segmentation ability of SAM,the proposed method not only demonstrates the effectiveness of the YOLOv8-Pose model in detecting keypoint coordinates of CSC leakage points from the perspective of application innovation but also establishes a novel approach for accurate segmentation of CSC leakage points through the“detect-then-segment”strategy,thereby providing a potential auxiliary means for the automatic and precise realtime localization of leakage points during traditional laser photocoagulation for CSC.
