Predicting instructed simulation and dissimulation when screening for depressive symptoms.

Predicting instructed simulation and dissimulation when screening for depressive symptoms. Eur Arch Psychiatry Clin Neurosci. 2018 Dec 12;: Authors: Goerigk S, Hilbert S, Jobst A, Falkai P, Bühner M, Stachl C, Bischl B, Coors S, Ehring T, Padberg F, Sarubin N Abstract The intentional distortion of test results presents a fundamental problem to self-report-based psychiatric assessment, such as screening for depressive symptoms. The first objective of the study was to clarify whether depressed patients like healthy controls possess both the cognitive ability and motivation to deliberately influence results of commonly used screening measures. The second objective was the construction of a method derived directly from within the test takers' responses to systematically detect faking behavior. Supervised machine learning algorithms posit the potential to empirically learn the implicit interconnections between responses, which shape detectable faking patterns. In a standardized design, faking bad and faking good were experimentally induced in a matched sample of 150 depressed and 150 healthy subjects. Participants completed commonly used questionnaires to detect depressive and associated symptoms. Group differences throughout experimental conditions were evaluated using linear mixed-models. Machine learning algorithms were trained on the test results and compared regarding their capacity to systematically predict distortions in response ...
Source: European Archives of Psychiatry and Clinical Neuroscience - Category: Psychiatry Tags: Eur Arch Psychiatry Clin Neurosci Source Type: research