a Department of Psychology & Philosophy , Sam Houston State University , Huntsville , TX , USA.
b Anna Freud National Centre for Children and Families , London , UK.
Attach Hum Dev. 2018 Feb;20(1):62-83. doi: 10.1080/14616734.2017.1378239. Epub 2017 Sep 26.
Assessing attachment in adolescents is important due to relations between insecurity and psychopathology. The child attachment interview (CAI) holds promise in this regard, but is time-consuming to code, which may render it inaccessible. The aim of this study was to develop computerized neural network models to predict attachment classifications on the CAI and to determine whether the models could achieve the CAI's benchmark qualification of 80% on reliability training cases. Four hundred and ninety interviews from inpatient adolescents served as model training and testing samples. The CAI's 30 standard reliability cases were treated as the independent holdout sample, in which the performance of the final models was evaluated against the 80% benchmark. Models demonstrated moderate accuracy and high correct classification rates, as compared to human coders. Performance was poorer when models were applied to the reliability training cases, but automated coding of the CAI holds promise for future development.
评估青少年的依恋关系很重要，因为不安全感和精神病理学之间存在关系。儿童依恋访谈（CAI）在这方面有很大的希望，但编码耗时，可能难以实现。本研究的目的是开发计算机神经网络模型来预测 CAI 的依恋分类，并确定模型是否可以达到 CAI 的 80%可靠性训练案例的基准资格。490 名住院青少年的访谈作为模型训练和测试样本。CAI 的 30 个标准可靠性案例作为独立的保留样本，在该样本中，最终模型的性能与 80%的基准进行了比较。与人类编码员相比，模型表现出中等的准确性和较高的正确分类率。当模型应用于可靠性训练案例时，性能较差，但 CAI 的自动编码具有未来发展的潜力。
