What's in a trauma? Using machine learning to unpack what makes an event traumatic

Abstract

What differentiates a trauma from an event that is merely upsetting? Wildly different definitions of trauma have been used in both formal (psychiatric) and informal (cultural, colloquial) settings. Yet there is a dearth of empirical work examining the features of events that individuals use to define an event as a ‘trauma.' First, a group of qualitative coders classified features (e.g., actual physical injury, loss of possessions) of 600 event descriptions (e.g., “was verbally harassed by a boss,” “watched a video of an adult being shot and killed”). Next, across two studies, machine learning was used to predict whether individuals rated event descriptions as ‘trauma’ or ‘traumatic’ in over 100,000 judgment tasks. In Study 1, examining continuous ratings from ‘not at all traumatic’ to ‘extremely traumatic,’ a cross-validated LASSO regression with polynomial features provided the best out-of-sample predictions (r2 = 0.76), outperforming ridge regression, support vector regression, and linear regression. In Study 2, using binary judgments, a random forest model accurately predicted out-of-sample individual responses (AUC = 0.96), outperforming a neural network and an AdaBoost ensemble classifier. The most important event features across the two studies were actual death, threat of death, and the presence of a human perpetrator. The most important human features in predicting judgments were political orientation and gender.