Waqas Ahmed, Naveed Sadiq, Aedma Kapil Kiran, Tariq Maryam, Afzaal Tayyaba
CMH Lahore Medical College & Institute of Dentistry, Lahore Cantt, Pakistan.
Human Development Research Foundation, Rawalpindi, Pakistan.
BMC Res Notes. 2018 Nov 1;11(1):782. doi: 10.1186/s13104-018-3876-6.
The clusters of participants with a homogeneous psychological make-up can be identified using sophisticated machine learning techniques such as the two-step clustering algorithm. It can also help us to identify the synergistic and additive effects of a range of psychometric variables. The identification of synergistic effect of this clustering of defense mechanism has significant practical implications as they share a certain variance. This study aims to identify the clusters of ego defenses and their relationship with academic performance and mental health outcome in medical students.
The high achievers scored higher on mature and neurotic defense styles and lower on immature than their counter parts. A higher proportion of medical students in high achievers group had normal scores on depressive symptoms than low achievers. While a majority among low achievers suffered from severe anxiety levels than high achievers group. High achievers scored higher on sublimation, humor, anticipation, suppression, pseudo-altruism, idealization, reaction formation, autistic fantasy, denial, and rationalization.
使用复杂的机器学习技术（如两步聚类算法）可以识别出心理构成同质的参与者群体。它还可以帮助我们识别一系列心理测量变量的协同和累加效应。这种防御机制聚类的协同效应识别具有重要的实际意义，因为它们存在一定的方差。本研究旨在识别医学生自我防御的类别及其与学业成绩和心理健康结果的关系。
成绩优异者在成熟和神经质防御方式上得分高于成绩相当者，在不成熟防御方式上得分低于成绩相当者。成绩优异组的医学生中，抑郁症状得分正常的比例高于成绩较差者。而成绩较差者中，患有严重焦虑水平的人数比成绩优异组多。成绩优异者在升华、幽默、预期、压抑、伪利他主义、理想化、反向形成、自闭症幻想、否认和合理化方面得分更高。
