未经评估:防御功能(即,用于管理真实或感知威胁的无意识过程)可能在各种精神病理学的发展中发挥作用。它通常通过观察者评级来评估,然而,人类对防御功能的编码是资源密集型和耗时的。这项研究的目的是开发一种机器学习方法,以自动从访谈笔录中编码防御机制。
UNASSIGNED:参与者包括患有暴食症的女性的临床样本(n=92)和没有暴食症的社区样本(n=66)。我们训练并评估了五个基于RoBERTa的模型,以检测嵌套在192次访谈中的16,785名面试官-参与者谈话对中的防御的存在。一个模型检测到任何防御的存在,虽然另外四个模型检测到了这个样本中最常见的防御(压抑,智能化,反应形成,undoing).
未经评估:这些模型能够区分防御(ROC-AUC.82-.90),但不够熟练,无法保证更换人类编码器(PR-AUC.28-.60)。进行后续分析以评估这些模型的其他实际用途。
UNASSIGNED:我们的机器学习模型可用于帮助程序员。未来的研究应该进行部署研究,以确定是否可以使用机器学习模型加快防御机制的人类编码。
Defensive functioning (i.e., unconscious process used to manage real or perceived threats) may play a role in the development of various psychopathologies. It is typically assessed via observer rating measures, however, human coding of defensive functioning is resource-intensive and time-consuming. The purpose of this study was to develop a machine learning approach to automate coding of defense mechanisms from interview transcripts.
Participants included a clinical sample of women with binge-eating disorder (n = 92) and a community sample without binge-eating disorder (n = 66). We trained and evaluated five RoBERTa-based models to detect the presence of defenses in 16,785 interviewer-participant talk-turn pairs nested within 192 interviews. A model detected the presence of any defense, while four additional models detected the most common defenses in this sample (repression, intellectualization, reaction formation, undoing).
The models were capable of distinguishing defenses (ROC-AUC .82-.90) but were not proficient enough to warrant replacing human coders (PR-AUC .28-.60). Follow-up analysis was performed to assess other practical uses of these models.
Our machine learning models could be used to assist coders. Future research should conduct a deployment study to determine if human coding of defense mechanisms can be expedited using machine learning models.