Harvard University, Cambridge, MA, USA.
University of Amsterdam, Amsterdam, The Netherlands.
Suicide Life Threat Behav. 2020 Oct;50(5):1065-1074. doi: 10.1111/sltb.12652. Epub 2020 Jul 20.
The Death/Suicide Implicit Association Test (IAT) is effective at detecting and prospectively predicting suicidal thoughts and behaviors. However, traditional IAT scoring procedures used in all prior studies (i.e., D-scores) provide an aggregate score that is inherently relative, obfuscating the separate associations (i.e., "Me = Death/Suicide," "Me = Life") that might be most relevant for understanding suicide-related implicit cognition. Here, we decompose the D-scores and validate a new analytic technique called the Decomposed D-scores ("DD-scores") that creates separate scores for each category ("Me," "Not Me") in the IAT. Across large online volunteer samples (N > 12,000), results consistently showed that a weakened association between "Me = Life" is more strongly predictive of having a history of suicidal attempts than is a stronger association between "Me = Death/Suicide." These findings replicated across three different versions of the IAT and were observed when calculated using both reaction times and error rates. However, among those who previously attempted suicide, a strengthened association between "Me = Death" is more strongly predictive of the recency of a suicide attempt. These results suggest that decomposing traditional IAT D-scores can offer new insights into the mental associations that may underlie clinical phenomena and may help to improve the prediction, and ultimately the prevention, of these clinical outcomes.
死亡/自杀内隐联想测验（IAT）在检测和预测自杀意念和行为方面非常有效。然而，以往所有研究中使用的传统 IAT 评分程序（即 D 分数）提供的是一种综合得分，这种得分本质上是相对的，掩盖了可能对理解与自杀相关的内隐认知最相关的单独关联（即“我=死亡/自杀”、“我=生命”）。在这里，我们对 D 分数进行了分解，并验证了一种新的分析技术，称为分解 D 分数（“DD 分数”），该技术为 IAT 中的每个类别（“我”、“非我”）创建了单独的分数。在大型在线志愿者样本中（N>12000），结果一致表明，“我=生命”之间的关联减弱与自杀未遂史的相关性更强，而“我=死亡/自杀”之间的关联增强则相关性较弱。这些发现通过三种不同版本的 IAT 得到了复制，并且在使用反应时间和错误率计算时也得到了观察。然而，在那些曾经尝试过自杀的人中，“我=死亡”之间的关联增强与自杀企图的近期程度相关性更强。这些结果表明，分解传统的 IAT D 分数可以为理解可能构成临床现象基础的心理关联提供新的见解，并可能有助于提高对这些临床结果的预测，最终有助于预防这些临床结果。
