Suppr超能文献

刻画中国本科生的共情-系统化特征:一种以人为本的方法。

Characterizing Chinese undergraduate students' empathizing-systemizing profiles: a person-centered approach.

作者信息

Qin Yishu, Zhang Da-Wei

机构信息

School of Educational Sciences, Yangzhou University, Yangzhou, China.

Department of Psychology, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Selangor, Malaysia.

出版信息

Front Psychol. 2024 Jul 15;15:1395560. doi: 10.3389/fpsyg.2024.1395560. eCollection 2024.

Abstract

While the empathizing-systemizing (E-S) theory provides a valuable framework for explaining gender differences in STEM majors, previous studies suffer from methodological issues (i.e., the arbitrary cut-off criteria and WEIRD sampling) as well as discrepancies in the behavioral correlates of E-S types. To address the gaps, this study utilized a 3-step latent profile analysis to identify naturally occurring E-S profiles in a Chinese sample and explored the predictors and distal outcomes of the identified profiles. The study recruited 785 (aged 18-25 years, 60% female) Chinese undergraduates. Results revealed five E-S profiles: . Controlling for socioeconomic status, being male predicted a higher likelihood of membership into the . Besides, membership to the and was associated with better intuitive physics performance. However, no significant variation was observed for social sensitivity performance across E-S profiles. Overall, our results partially conformed to previous findings, highlighting the importance of cultural adaptation and methodological considerations when classifying students' cognitive types.

摘要

虽然共情-系统化(E-S)理论为解释STEM专业中的性别差异提供了一个有价值的框架,但以往的研究存在方法学问题(即任意的截断标准和怪异样本)以及E-S类型行为相关性的差异。为了填补这些空白,本研究采用了三步潜在剖面分析来识别中国样本中自然出现的E-S剖面,并探讨所识别剖面的预测因素和远端结果。该研究招募了785名中国本科生(年龄在18至25岁之间,60%为女性)。结果揭示了五种E-S剖面: 。在控制社会经济地位后,男性被预测更有可能属于 。此外,属于 和 的成员在直观物理表现方面更好。然而,在E-S剖面中,社会敏感性表现没有观察到显著差异。总体而言,我们的结果部分符合先前的发现,突出了文化适应和分类学生认知类型时方法学考虑的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ede1/11284084/7130ec26842f/fpsyg-15-1395560-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验