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Article(id=1325532504001889266, tenantId=1146029695717560320, journalId=1256213402557890569, issueId=1325531848021140417, articleNumber=null, orderNo=null, doi=10.16835/j.cnki.1000-9817.2025335, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1748361600000, receivedDateStr=2025-05-28, revisedDate=1758384000000, revisedDateStr=2025-09-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1793961176156, onlineDateStr=2026-11-06, pubDate=1764000000000, pubDateStr=2025-11-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793961176156, onlineIssueDateStr=2026-11-06, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793961176156, creator=13701087609, updateTime=1793961176156, updator=13701087609, issue=Issue{id=1325531848021140417, tenantId=1146029695717560320, journalId=1256213402557890569, year='2025', volume='46', issue='11', pageStart='1521', pageEnd='1672', issueExtLink='null', onlineDate='null', pubDate='1764000000000', pubDateStr='2025-11-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793961019759, creator='13701087609', updateTime=1793961019759, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1610, endPage=1614, ext={EN=ArticleExt(id=1325532508586263540, articleId=1325532504001889266, tenantId=1146029695717560320, journalId=1256213402557890569, language=EN, title=Factors associated with high-level fear of negative evaluation and construction of a risk prediction model among junior high school students, columnId=1259565855189418448, journalTitle=Chinese Journal of School Health, columnName=Mental Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the factors associated with high-level fear of negative evaluation (FNE) among junior high school students and to construct a nomogram risk prediction model, so as to provide scientific tools for psychological health intervention for junior high school students.

Methods

A convenience sampling combined with cluster random sampling method was used to select 5 485 junior high school students from 4 cities (Wuhan, Huanggang, Xianning and Xiaogan) for an online questionnaire survey in March 2025. The total sample was randomly split into a training set (n=3 839) and a validation set (n=1 646). Univariate analysis was performed in the training set using Chi-square test and t-test. Variables with statistical significance were subsequently included in multivariate Logistic regression to identify independent predictors and to construct a nomogram-based risk prediction model. The discriminative ability and clinical utility of the model were evaluated in the validation set using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

There were 1 649 junior high school students with low-level FNE and 2 190 with high-level FNE in the training set. The self-control ability of junior high school students with low-level and high-level FNE showed a statistically significant difference (23.96±3.96, 21.48±3.37, t=25.15, P < 0.01). Statistically significant differences in the detection rate of high-level FNE were observed among junior high school students with different genders, family types, parenting styles, academic rankings, psychological flexibility, mobile phone addiction tendencies, emotional management training, exercise frequency, left-behind experiences, and places of origin (χ2=82.01-1 126.68, all P < 0.01). The results of Logistic regression analysis revealed that, the following factors were identified as significant factors influencing high-level FNE among junior high school students: exercise frequency (OR=0.21, 95%CI=0.17-0.26); parenting style (OR=0.48, 95%CI=0.40-0.58); left-behind experience (OR=3.88, 95%CI=3.27-4.61); smartphone addiction proneness (OR=2.19, 95%CI=0.89-0.93); self-control ability (OR=0.91, 95%CI=0.89-0.93); and psychological flexibility (OR=0.16, 95%CI=0.10-0.28) (all P < 0.05). The AUC for the training and validation set were 0.88 (95%CI=0.87-0.89) and 0.87 (95%CI=0.85-0.89), respectively. The Hosmer-Lemeshow goodness-of-fit test yielded χ2=8.57, 15.20 (both P>0.05).

Conclusion

The risk prediction model with high-level FNE demonstrates good accuracy and can assist educators and parents in timely screening of junior high school students with high-level FNE, thereby providing a basis for implementing targeted interventions.

, authors=Yaxin LI, Miaomiao LIANG, Shiqi WANG, Yajun LIU, Jiayi GU, Hongjuan CHANG*, authorsList=Yaxin LI, Miaomiao LIANG, Shiqi WANG, Yajun LIU, Jiayi GU, Hongjuan CHANG, authorCompany=null, correspAuthors=Hongjuan CHANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2025 Chinese Journal of School Health. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1325532518308660216, articleId=1325532504001889266, tenantId=1146029695717560320, journalId=1256213402557890569, language=CN, title=初中生高水平负面评价恐惧相关因素及风险预测模型构建, columnId=1259565858817491410, journalTitle=中国学校卫生, columnName=心理卫生, runingTitle=null, highlight=null, articleAbstract=
目的

分析初中生高水平负面评价恐惧(FNE)的相关因素并构建列线图风险预测模型，为初中生心理健康干预工作提供科学工具。

方法

2025年3月，采用方便抽样与整群随机抽样相结合的方法在湖北省武汉市、黄冈市、咸宁市、孝感市的4所中学选取5 485名初中生进行线上问卷调查，按照7∶3的比例随机划分为训练集(n=3 839)和验证集(n=1 646)。在训练集中采用χ2检验和t检验进行单因素分析，将有统计学意义的变量纳入Logistic回归模型进行多因素分析，据此构建列线图风险预测模型，通过受试者工作特征曲线下面积(AUC)、校准曲线及决策曲线分析(DCA)评估模型的判别效能与临床适用性。

结果

训练集中低水平FNE 1 649名，高水平FNE 2 190名，低水平和高水平初中生自我控制能力(23.96±3.96，21.48±3.37)差异有统计学意义(t=25.15，P＜0.01)。不同性别、家庭类型、家庭教养方式、成绩排名、心理灵活性、手机成瘾倾向、情绪管理培训、运动频率、留守经历和生源地初中生高水平FNE检出率差异均有统计学意义(χ2=82.01~1 126.68，P值均＜0.01)。Logistic回归分析结果显示，运动频率(OR=0.21，95%CI=0.17~0.26)、家庭教养方式(OR=0.48，95%CI=0.40~0.58)、留守经历(OR=3.88，95%CI=3.27~4.61)、手机成瘾倾向(OR=2.19，95%CI=0.89~0.93)、自我控制能力(OR=0.91，95%CI=0.89~0.93)、心理灵活性(OR=0.16，95%CI=0.10~0.28)是初中生高水平FNE的相关因素(P值均＜0.05)。训练集和验证集的AUC分别为0.88(95%CI=0.87~0.89)、0.87(95%CI=0.85~0.89)，Hosmer-Lemeshow拟合优度检验χ2值分别为8.57，15.20(P值均>0.05)。

结论

高水平FNE风险预测模型具有良好准确度，可辅助教育工作者和家长及时筛查出高水平FNE的初中生，为开展针对性干预提供依据。

, authors=李亚昕, 梁苗苗, 王诗琦, 刘雅君, 谷嘉怡, 常红娟*, authorsList=李亚昕, 梁苗苗, 王诗琦, 刘雅君, 谷嘉怡, 常红娟, authorCompany=null, correspAuthors=常红娟, authorNote=

李亚昕(2000-)，男，山西长治人，在读硕士，主要研究方向为儿童青少年卫生学

, correspAuthorsNote=
常红娟，E-mail:
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(in Chinese), articleTitle=null, refAbstract=null)], funds=[Fund(id=1325532562432737323, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325532504001889266, awardId=null, language=CN, fundingSource=国家自然科学基金项目(81803252)；湖北省教育厅重点项目(24D083，2024242), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1325532522473604089, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325532504001889266, xref=null, ext=[AuthorCompanyExt(id=1325532522502964218, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325532504001889266, companyId=1325532522473604089, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Medicine, Wuhan University of Science and Technology, Wuhan 430081, Hubei Province, China), AuthorCompanyExt(id=1325532522536518651, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325532504001889266, companyId=1325532522473604089, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=武汉科技大学医学院，湖北 430081)])], figs=[ArticleFig(id=1325532554354507811, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325532504001889266, language=EN, label=Fig.1, caption=High-level negative evaluation fear risk prediction model for junior high school students, figureFileSmall=Q5ew7SCi8t1wpIlayeo+Qg==, figureFileBig=mhcoAOh5aL7WUlTfzjnzwQ==, tableContent=null), ArticleFig(id=1325532555214340132, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325532504001889266, language=CN, label=图1, caption=初中生高水平负面评价恐惧风险预测模型, figureFileSmall=Q5ew7SCi8t1wpIlayeo+Qg==, figureFileBig=mhcoAOh5aL7WUlTfzjnzwQ==, tableContent=null), ArticleFig(id=1325532558263599141, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325532504001889266, language=EN, label=Tab.1, caption=

Univariate analysis of influencing factors for high-level fear of negative evaluation among junior high school students

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类人数高水平负面评价恐惧人数χ2值P值
性别女1 762884(50.17)89.57＜0.01
男2 0771 306(62.90)
家庭类型核心家庭1 497763(51.00)1 672.82＜0.01
单亲家庭541528(97.60)
重组家庭602571(94.85)
隔代家庭1 199328(27.44)
家庭教养方式严厉1 128743(65.87)378.94＜0.01
溺爱193165(85.49)
温和1 884837(44.43)
变化无常634445(70.91)
成绩排名上游782519(66.37)172.47＜0.01
中游1 453668(45.97)
下游1 6041 003(62.53)
心理灵活性低水平184163(88.59)1 126.68＜0.01
中等水平2 2151 618(73.05)
高水平1 440409(28.40)
手机成瘾倾向轻度3 2281 670(51.73)338.82＜0.01
中度546459(84.07)
重度6561(93.85)
情绪管理培训未参加过920603(65.54)94.92＜0.01
偶尔参加2 6671 491(55.91)
经常参加25296(38.09)
运动频率偶尔1 234940(76.18)637.78＜0.01
有时1 259770(61.16)
经常1 346480(35.66)
留守经历无1 771644(36.36)818.25＜0.01
有2 0681 546(74.76)
生源地城镇2 0091 262(62.82)82.01＜0.01
农村1 830928(50.71)
), ArticleFig(id=1325532559056322598, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325532504001889266, language=CN, label=表1, caption=

初中生高水平负面评价恐惧相关因素的单因素分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类人数高水平负面评价恐惧人数χ2值P值
性别女1 762884(50.17)89.57＜0.01
男2 0771 306(62.90)
家庭类型核心家庭1 497763(51.00)1 672.82＜0.01
单亲家庭541528(97.60)
重组家庭602571(94.85)
隔代家庭1 199328(27.44)
家庭教养方式严厉1 128743(65.87)378.94＜0.01
溺爱193165(85.49)
温和1 884837(44.43)
变化无常634445(70.91)
成绩排名上游782519(66.37)172.47＜0.01
中游1 453668(45.97)
下游1 6041 003(62.53)
心理灵活性低水平184163(88.59)1 126.68＜0.01
中等水平2 2151 618(73.05)
高水平1 440409(28.40)
手机成瘾倾向轻度3 2281 670(51.73)338.82＜0.01
中度546459(84.07)
重度6561(93.85)
情绪管理培训未参加过920603(65.54)94.92＜0.01
偶尔参加2 6671 491(55.91)
经常参加25296(38.09)
运动频率偶尔1 234940(76.18)637.78＜0.01
有时1 259770(61.16)
经常1 346480(35.66)
留守经历无1 771644(36.36)818.25＜0.01
有2 0681 546(74.76)
生源地城镇2 0091 262(62.82)82.01＜0.01
农村1 830928(50.71)
), ArticleFig(id=1325532559509307433, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325532504001889266, language=EN, label=Tab.2, caption=

Multivariate Logistic regression analysis of negative evaluation level among junior high school students(n=3 839)

, figureFileSmall=null, figureFileBig=null, tableContent=
自变量选项β值标准误Wald χ2值P值OR值(95%CI)
自我控制能力-0.100.0153.54＜0.010.91(0.89~0.93)
心理灵活性中等水平-0.210.260.650.420.81(0.48~1.36)
高水平-1.810.2743.71＜0.010.16(0.10~0.28)
手机成瘾倾向中度0.780.1432.47＜0.012.19(1.67~2.87)
重度1.630.539.34＜0.015.10(1.79~14.51)
运动频率有时-0.120.111.190.280.89(0.72~1.10)
经常-1.570.11215.49＜0.010.21(0.17~0.26)
家庭教养方式溺爱0.580.255.680.021.79(1.11~2.90)
温和-0.730.1055.87＜0.010.48(0.40~0.58)
变化无常0.130.130.970.321.14(0.88~1.48)
留守经历有1.360.09238.63＜0.013.88(3.27~4.61)
), ArticleFig(id=1325532561140891690, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325532504001889266, language=CN, label=表2, caption=

初中生负面评价恐惧的多因素Logistic回归分析(n=3 839)

, figureFileSmall=null, figureFileBig=null, tableContent=
自变量选项β值标准误Wald χ2值P值OR值(95%CI)
自我控制能力-0.100.0153.54＜0.010.91(0.89~0.93)
心理灵活性中等水平-0.210.260.650.420.81(0.48~1.36)
高水平-1.810.2743.71＜0.010.16(0.10~0.28)
手机成瘾倾向中度0.780.1432.47＜0.012.19(1.67~2.87)
重度1.630.539.34＜0.015.10(1.79~14.51)
运动频率有时-0.120.111.190.280.89(0.72~1.10)
经常-1.570.11215.49＜0.010.21(0.17~0.26)
家庭教养方式溺爱0.580.255.680.021.79(1.11~2.90)
温和-0.730.1055.87＜0.010.48(0.40~0.58)
变化无常0.130.130.970.321.14(0.88~1.48)
留守经历有1.360.09238.63＜0.013.88(3.27~4.61)
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初中生高水平负面评价恐惧相关因素及风险预测模型构建
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李亚昕 , 梁苗苗 , 王诗琦 , 刘雅君 , 谷嘉怡 , 常红娟 *
中国学校卫生 | 心理卫生 2025,46(11): 1610-1614
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中国学校卫生 |心理卫生 2025 , 46 (11) : 1610 -1614
初中生高水平负面评价恐惧相关因素及风险预测模型构建
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李亚昕, 梁苗苗, 王诗琦, 刘雅君, 谷嘉怡, 常红娟*
作者信息
  • 武汉科技大学医学院，湖北 430081
通讯作者:
常红娟，E-mail:
作者简介:

李亚昕(2000-)，男，山西长治人，在读硕士，主要研究方向为儿童青少年卫生学

Factors associated with high-level fear of negative evaluation and construction of a risk prediction model among junior high school students
Yaxin LI, Miaomiao LIANG, Shiqi WANG, Yajun LIU, Jiayi GU, Hongjuan CHANG*
Affiliations
  • School of Medicine, Wuhan University of Science and Technology, Wuhan 430081, Hubei Province, China
出版时间: 2025-11-25 doi: 10.16835/j.cnki.1000-9817.2025335
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摘要
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目的

分析初中生高水平负面评价恐惧(FNE)的相关因素并构建列线图风险预测模型，为初中生心理健康干预工作提供科学工具。

方法

2025年3月，采用方便抽样与整群随机抽样相结合的方法在湖北省武汉市、黄冈市、咸宁市、孝感市的4所中学选取5 485名初中生进行线上问卷调查，按照7∶3的比例随机划分为训练集(n=3 839)和验证集(n=1 646)。在训练集中采用χ2检验和t检验进行单因素分析，将有统计学意义的变量纳入Logistic回归模型进行多因素分析，据此构建列线图风险预测模型，通过受试者工作特征曲线下面积(AUC)、校准曲线及决策曲线分析(DCA)评估模型的判别效能与临床适用性。

结果

训练集中低水平FNE 1 649名，高水平FNE 2 190名，低水平和高水平初中生自我控制能力(23.96±3.96，21.48±3.37)差异有统计学意义(t=25.15，P＜0.01)。不同性别、家庭类型、家庭教养方式、成绩排名、心理灵活性、手机成瘾倾向、情绪管理培训、运动频率、留守经历和生源地初中生高水平FNE检出率差异均有统计学意义(χ2=82.01~1 126.68，P值均＜0.01)。Logistic回归分析结果显示，运动频率(OR=0.21，95%CI=0.17~0.26)、家庭教养方式(OR=0.48，95%CI=0.40~0.58)、留守经历(OR=3.88，95%CI=3.27~4.61)、手机成瘾倾向(OR=2.19，95%CI=0.89~0.93)、自我控制能力(OR=0.91，95%CI=0.89~0.93)、心理灵活性(OR=0.16，95%CI=0.10~0.28)是初中生高水平FNE的相关因素(P值均＜0.05)。训练集和验证集的AUC分别为0.88(95%CI=0.87~0.89)、0.87(95%CI=0.85~0.89)，Hosmer-Lemeshow拟合优度检验χ2值分别为8.57，15.20(P值均>0.05)。

结论

高水平FNE风险预测模型具有良好准确度，可辅助教育工作者和家长及时筛查出高水平FNE的初中生，为开展针对性干预提供依据。

恐惧  /  精神卫生  /  模型, 结构  /  学生
Objective

To analyze the factors associated with high-level fear of negative evaluation (FNE) among junior high school students and to construct a nomogram risk prediction model, so as to provide scientific tools for psychological health intervention for junior high school students.

Methods

A convenience sampling combined with cluster random sampling method was used to select 5 485 junior high school students from 4 cities (Wuhan, Huanggang, Xianning and Xiaogan) for an online questionnaire survey in March 2025. The total sample was randomly split into a training set (n=3 839) and a validation set (n=1 646). Univariate analysis was performed in the training set using Chi-square test and t-test. Variables with statistical significance were subsequently included in multivariate Logistic regression to identify independent predictors and to construct a nomogram-based risk prediction model. The discriminative ability and clinical utility of the model were evaluated in the validation set using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

There were 1 649 junior high school students with low-level FNE and 2 190 with high-level FNE in the training set. The self-control ability of junior high school students with low-level and high-level FNE showed a statistically significant difference (23.96±3.96, 21.48±3.37, t=25.15, P < 0.01). Statistically significant differences in the detection rate of high-level FNE were observed among junior high school students with different genders, family types, parenting styles, academic rankings, psychological flexibility, mobile phone addiction tendencies, emotional management training, exercise frequency, left-behind experiences, and places of origin (χ2=82.01-1 126.68, all P < 0.01). The results of Logistic regression analysis revealed that, the following factors were identified as significant factors influencing high-level FNE among junior high school students: exercise frequency (OR=0.21, 95%CI=0.17-0.26); parenting style (OR=0.48, 95%CI=0.40-0.58); left-behind experience (OR=3.88, 95%CI=3.27-4.61); smartphone addiction proneness (OR=2.19, 95%CI=0.89-0.93); self-control ability (OR=0.91, 95%CI=0.89-0.93); and psychological flexibility (OR=0.16, 95%CI=0.10-0.28) (all P < 0.05). The AUC for the training and validation set were 0.88 (95%CI=0.87-0.89) and 0.87 (95%CI=0.85-0.89), respectively. The Hosmer-Lemeshow goodness-of-fit test yielded χ2=8.57, 15.20 (both P>0.05).

Conclusion

The risk prediction model with high-level FNE demonstrates good accuracy and can assist educators and parents in timely screening of junior high school students with high-level FNE, thereby providing a basis for implementing targeted interventions.

Fear  /  Mental health  /  Models, structural  /  Students
李亚昕, 梁苗苗, 王诗琦, 刘雅君, 谷嘉怡, 常红娟. 初中生高水平负面评价恐惧相关因素及风险预测模型构建. 中国学校卫生, 2025 , 46 (11) : 1610 -1614 . DOI: 10.16835/j.cnki.1000-9817.2025335
Yaxin LI, Miaomiao LIANG, Shiqi WANG, Yajun LIU, Jiayi GU, Hongjuan CHANG. Factors associated with high-level fear of negative evaluation and construction of a risk prediction model among junior high school students[J]. Chinese Journal of School Health, 2025 , 46 (11) : 1610 -1614 . DOI: 10.16835/j.cnki.1000-9817.2025335
正文
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负面评价恐惧(fear of negative evaluation, FNE)是指个体预期他人可能给予的消极评价所产生的过度担忧和恐惧[1]，初中生处于心理发展关键期，社交互动显著增多与自我意识增强使得该群体对他人评价尤为敏感。高水平FNE不仅构成社交焦虑与行为回避的重要诱因[2]，更可能通过负性情绪认知引发学习倦怠[3]，对学业效能与心理健康造成多维度、持续性负面影响。过往研究表明，FNE的形成与发展并非单一因素作用的结果，是受到个体心理素质、家庭环境、学业压力及生活方式等因素的复杂影响[4]，严重阻碍初中生的社会适应与人格发展。因此，理解这些关键影响因素的交互作用对于精准识别高风险个体至关重要，对初中生群体中高水平FNE个体实施早期识别与针对性干预措施具有心理健康维护和学业发展促进方面的双重实践价值。
列线图作为一种可视化风险预测模型[5]能够整合多种影响因素并直观呈现风险概率，在临床医学领域已广泛使用[6]。将列线图引入初中生高水平FNE的风险预测可以弥补传统量表筛查的局限性[7]，独特优势在于能够整合多维度影响因素进行风险预测并可视化输出，便于教育工作者和家长快速识别高风险初中生，实现早期干预[8]。本研究旨在构建初中生高水平FNE的风险预测模型，通过精准识别高风险个体以便及时采取针对性干预措施，降低FNE对初中生心理健康和学业发展的负面影响，为初中生心理健康干预工作提供科学工具。
1　对象与方法
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1.1　对象
于2025年3月，采用方便抽样法选取湖北省武汉市、黄冈市、咸宁市、孝感市的4所中学。在每所中学内，采用整群随机抽样法, 以班级为抽样框，整群抽取若干班级，将抽中班级的所有在籍初中生纳入研究，征得其知情同意后，共发放问卷5 600份，收回有效问卷5 485份，有效回收率为97.95%。纳入标准：年龄为12~15岁的在校初中生；本人及监护人自愿参与研究，由监护人签署知情同意书。排除标准：经医院确诊患有严重躯体疾病或精神心理疾病；调查期间处于休学、退学状态或因身体、家庭等特殊原因无法正常参与调查。使用R 4.4.1软件中内置的随机函数按照7∶3的比例随机划分为训练集(3 839名)和验证集(1 646名)。本研究通过武汉科技大学医学院医学伦理委员会审查(批号：伦审科第2023177号)。
1.2　调查工具
1.2.1　一般资料调查问卷
由研究者自行设计，包括性别、年龄、学习成绩排名、家庭类型(核心家庭/单亲家庭/重组家庭/隔代家庭)、家庭教养方式(严厉/溺爱/温和/变化无常)、运动频率、情绪管理培训、留守经历等社会人口学资料。学习成绩排名参考2024年下学年期末成绩班级排名，排名前25%为上游，>25%~75%为中游，后25%为下游。将近3个月内运动频率每周≥3次判定为经常/每周，1~2次判定为有时，每月1次及以下判定为偶尔。在近3个月内情绪管理培训中，每周参加1次判定为经常参加，1~3个月参加1次为有时参加[9]。
1.2.2　简明负面评价恐惧量表(Brief Fear of Negative Evaluation Scale，BFNES)
共12个条目，采用Likert 5点计分法，从“完全不符合”到“极其符合”分别计1~5分，包括8个对FNE的正向描述条目和4个反向描述条目，总分12~60分。参照汪星宇等[9]研究，将总分≥36分视为高水平FNE，＜36分视为低水平FNE。该问卷得分越高表明FNE水平越高。本研究中该问卷的Cronbach α系数为0.74，分半信度为0.75。
1.2.3　心理灵活性量表
该量表由接纳与行动问卷第二版的中文版(Acceptance and Action Questionn-aire-Second Edition，AAQ-Ⅱ)和修订后的认知融合分问卷(Cognitive Fusion Questionnaire, CFQ-F)两部分构成。其中AAQ-Ⅱ用于测量个体的经验回避水平[10]，共包含7个条目，采用Likert 7点计分(1=“从未”至7=“总是”)，总分越高表示经验回避程度越高。该量表在本研究中的Cronbach α系数为0.88，重测信度为0.80。CFQ-F共9个条目，单一维度，采用7点计分(1=“从未”至7=“总是”)，得分越高表明认知融合程度越高，心理灵活性水平越差。其内部一致性系数为0.92，重测信度为0.67，信效度符合测量要求。心理灵活性总分为两量表得分之和，总分16~112分，划分为3个水平：16~48分为高水平，49~80分为中等水平，81~112分为低水平。两量表共同从经验回避和认知融合2个维度综合评估个体的心理灵活性[11]。
1.2.4　手机成瘾问卷(Mobile Phone Addiction Index, MPAI)
共17题，如果被试17道题中有8题是肯定回答，则被界定为手机成瘾。量表采用1=“几乎没有”到5=“总是”计分，得分越高，表明对手机的成瘾倾向越高[12]。总分为17~85分，＜34分为无手机成瘾倾向，34~51分为轻度手机成瘾倾向，52~68分为中度手机成瘾倾向，69~85分为重度手机成瘾倾向。该量表的Cronbach α系数为0.87。
1.2.5　简式自我控制量表(Brief Self-control Scale, BSCS)
共7个条目，包括自律性(条目1，3，5)及冲动控制(条目2，4，6，7，反向计分)2个维度。量表采取Likert 5级评分，“完全不符合”=1至“完全符合”=5)，总分7~35分，得分越高表示自我控制水平越高[13]。本研究中该量表的Cronbach α系数为0.83。
1.3　质量控制
正式发放问卷前，先进行预调查，检验并完善相关流程。调查开展前为研究对象解释研究目的、意义、问卷填写方法及注意事项，确保受试者充分知情。为确保问卷的填写质量，必作答项完成后，方可提交问卷。
1.4　统计学分析
所有模型构建步骤(包括单因素分析及多因素Logistic回归)均在训练集内完成。验证集数据仅用于最终评估模型的判别效能、校准度及临床实用性。选用SPSS 26.0软件分析数据，连续性变量预先进行正态检验，对服从正态分布的连续变量采用(x±s)描述，两组间比较采用t检验。计数资料进行χ2检验，筛选后将具有统计学意义的变量纳入多因素Logistic回归分析。检验水准α=0.05。在R Studio软件(版本4.4.1)使用Nomogram函数对高水平FNE进行风险预测并绘制列线图进行可视化呈现[14]。模型验证在验证集中进行，通过Bootstrap法进行1 000次重复抽样完成内部验证，采用Calibration校准曲线评估模型预测与实际结果的一致性。绘制ROC曲线并计算曲线下面积(area under the curve, AUC)以评价模型的判别效能。采用Hosmer-Lemeshow检验评价模型拟合优度，当P＞0.05时认为模型拟合良好。
2　结果
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2.1　初中生FNE检出情况
训练集中低水平FNE 1 649名，高水平FNE 2 190名，低水平和高水平FNE初中生自我控制能力(23.96±3.96，21.48±3.37)差异有统计学意义(t=25.15，P＜0.01)。不同性别、家庭类型、家庭教养方式、成绩排名、心理灵活性、手机成瘾倾向、情绪管理培训、运动频率、留守经历和生源地初中生高水平FNE检出率差异均有统计学意义(P值均＜0.01)。见表1。
2.2　初中生FNE水平多因素Logistic回归分析
将单因素分析中有统计学意义的变量纳入Logistic回归分析，将FNE水平(低=0，高=1)作为因变量，结果表明，自我控制能力、心理灵活性、手机成瘾倾向、运动频率、家庭教养方式、留守经历为FNE的相关因素(P值均＜0.05)，见表2。
2.3　初中生高水平FNE的列线图模型构建与验证
基于多因素Logistic回归分析结果，构建初中生高水平FNE列线图风险预测模型，见图1。该模型的区分度在训练集和验证集中均得到了验证，结果显示，训练集AUC=0.88(95%CI=0.87~0.89)，灵敏度为76.89%，特异度81.29%；验证集的AUC=0.87(95%CI=0.85~0.89)，灵敏度为75.53%，特异度为81.42%。训练集和验证集的Hosmer-Lemeshow检验显示，χ训练集2=8.57，χ验证集2=15.20，P值均>0.05，表明拟合度良好。训练集和验证集的校准曲线均与对角线拟合良好，表示模型校准度与泛化性良好。本研究采用Bootstrap法进行内部验证，C-统计量为0.87，表明该列线图模型对初中生高水平FNE的预测效能具有较好的区分度、校准度及实用性。
3　讨论
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本研究通过Logistic回归分析识别出初中生高水平FNE的多个相关因素，并进一步从生理(运动频率)、家庭(家庭教养方式、留守经历)和心理(手机成瘾倾向、自我控制能力、心理灵活性)3个维度对其作用机制进行深入探讨。
在生理维度上，运动频率是重要的正向相关因素，较高的运动频率降低了个体对负面评价的恐惧水平，与已有研究认为运动有助于提升情绪调节能力和社交自信的观点一致[15-16]。内在机制可能包括：规律运动增强前额叶功能，改善认知灵活性，从而减少对社交信号的过度敏感和误解[17]，且运动获得的身体掌控感与成就感可提升社交自信[18]，增强应对负面评价的信心；运动不足则会抑制前额叶功能，使个体更易将中性社交信号误解为威胁[19]。因此，建议将适度运动纳入初中生日常安排，作为预防FNE的基础性措施，有助于促进身心恢复与情绪稳定，降低对负面评价的敏感性。
家庭维度的影响体现在家庭教养方式和留守经历两方面[20]。不同的教养方式通过差异化路径影响初中生对他人评价的感知与应对：严厉型教养容易诱发完美主义心态[21]，导致个体过度关注他人评价；溺爱型教养削弱个体挫折应对能力[22]，导致个体对外界反馈异常敏感甚至应对不良；变化无常型教养方式会破坏初中生的安全感，诱发交感神经亢进状态与对他人评价的灾难化认知倾向[23]。基于此，建议家长采取温和教养方式，不仅兼顾青春期自主性需求与必要引导，还需要提供情感支持，以帮助初中生建立稳定的心理预期，缓解初中生高水平FNE的关键措施。留守经历会增加个体FNE风险，反映长期亲子分离导致情感支持缺失[24]，尤其在青少年社会敏感性发展阶段会使个体对他人评价过度敏感[25]。建议通过家庭教育和家校协作机制，增强情感联结与心理支持，例如建立稳定的亲子沟通渠道和提供教养指导。
在心理维度中，手机成瘾倾向、自我控制能力和心理灵活性共同构成了影响FNE的关键心理机制。手机成瘾往往使个体暴露于社交媒体中经修饰的“完美形象”，易引发社会比较和自我怀疑，进而加深对现实评价的焦虑[26-27]。而自我控制能力和心理灵活性则发挥保护作用。自我控制能力缺乏的个体通常难以抑制消极思维，易陷入放大焦虑的“反刍思维”。心理灵活性较低的个体可能无法灵活调整认知策略，往往僵化地解读他人评价，例如将中性评价误解为否定评价，导致恐惧情绪持续累积。以上3类因素交互作用于个体，突显了在心理干预中需兼顾行为习惯与认知策略的双重调整。
本研究虽然在心理健康评估方法上克服了传统量表法的灵敏度局限[28-29]，通过可视化形式将抽象心理风险转化为直观的量化指标。然而本研究仍存在一定局限性：数据仅来自于武汉市及其周边城市，可能影响模型的泛化能力；模型虽能识别风险，但尚未结合具体干预方案验证实际改善效能。未来研究者可结合纵向设计与多模态数据优化模型的预测精度，推动FNE防控向精准化发展。
建议今后的干预工作从系统视角出发，整合校园运动促进、家庭教养指导、手机使用管理与认知灵活性训练等多层面策略，并将预测模型与校园心理健康信息系统融合，推动干预策略从“普适宣教”向“精准预警—分层干预”转变，从根本上提升学生应对负面评价的心理韧性与行为策略，阻断FNE向严重心理问题的发展路径。
基金
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  • 国家自然科学基金项目(81803252)；湖北省教育厅重点项目(24D083，2024242)
文献
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参考文献 引证文献
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2025年第46卷第11期
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doi: 10.16835/j.cnki.1000-9817.2025335
  • 接收时间：2025-05-28
  • 首发时间：2026-11-06
  • 出版时间：2025-11-25
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  • 收稿日期：2025-05-28
  • 修回日期：2025-09-21
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国家自然科学基金项目(81803252)；湖北省教育厅重点项目(24D083，2024242)
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    武汉科技大学医学院，湖北 430081

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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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