Abstract
This article introduces a new hybrid intake procedure developed for posttraumatic stress disorder (PTSD) screening, which combines an automated textual assessment of respondents’ self-narratives and item-based measures that are administered consequently. Text mining technique and item response modeling were used to analyze long constructed response (i.e., self-narratives) and responses to standardized questionnaires (i.e., multiple choices), respectively. The whole procedure is combined in a Bayesian framework where the textual assessment functions as prior information for the estimation of the PTSD latent trait. The purpose of this study is twofold: first, to investigate whether the combination model of textual analysis and item-based scaling could enhance the classification accuracy of PTSD, and second, to examine whether the standard error of estimates could be reduced through the use of the narrative as a sort of routing test. With the sample at hand, the combination model resulted in a reduction in the misclassification rate, as well as a decrease of standard error of latent trait estimation. These findings highlight the benefits of combining textual assessment and item-based measures in a psychiatric screening process. We conclude that the hybrid test design is a promising approach to increase test efficiency and is expected to be applicable in a broader scope of educational and psychological measurement in the future.
Highlights
Epidemiological research on mental illnesses such as posttraumatic stress disorder (PTSD) requires efficient methods to identify cases in large population-based samples (Shrout and Yager, 1989) because the diagnosis of the disorder is difficult to make and can involve expensive testing
We propose a two-stage hybrid test design using a Bayesian approach to combine text mining and item response modeling in one systematic framework, where an automated score based on textual analysis serves as input for a prior distribution of a latent trait associated with PTSD that is measured by a number of questionnaire items using an item response theory (IRT) model (Rasch, 1960; Lord, 1980)
The informative prior distribution of the PTSD latent trait was defined as θn|yn ∼ N(−0.41 + 1.44yn, 3.57)
Summary
Epidemiological research on mental illnesses such as posttraumatic stress disorder (PTSD) requires efficient methods to identify cases in large population-based samples (Shrout and Yager, 1989) because the diagnosis of the disorder is difficult to make and can involve expensive testing. In addition to trauma exposure screeners, abbreviated PTSD symptom screeners are frequently used to determine the need for more in-depth clinical interviews (Lancaster et al, 2016) These include the Primary Care PTSD Screen (PC-PTSD; Prins and Ouimette, 2004), the Short Form of the PTSD Checklist-Civilian Version (Lang and Stein, 2005), the Trauma Screening Questionnaire (TSQ; Brewin et al, 2000), and the Short Post-Traumatic Stress Disorder Rating Interview (SPRINT; Connor and Davidson, 2001). These instruments ideally contain the minimal number of items necessary for accurate case identification, have simple decision rules to determine who passes and fails the screening, and are applicable to populations with varying prevalence of PTSD and experiencing different traumas (see more in reviews by Brewin, 2005; Lancaster et al, 2016)
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