The Development of a Short Version of the SIMS Using Machine Learning to Detect Feigning in Forensic Assessment.

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From: Psychological Injury and Law(Vol. 14, Issue 1)
Publisher: Springer
Document Type: Report; Brief article
Length: 236 words

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Abstract :

Keywords: SIMS; Psychic damage; Malingering; Machine learning; Feature selection Abstract In the present study, we applied machine learning techniques to evaluate whether the Structured Inventory of Malingered Symptomatology (SIMS) can be reduced in length yet maintain accurate discrimination between consistent participants (i.e., presumed truth tellers) and symptom producers. We applied machine learning item selection techniques on data from Mazza et al. (2019c) to identify the minimum number of original SIMS items that could accurately distinguish between consistent participants, symptom accentuators, and symptom producers in real personal injury cases. Subjects were personal injury claimants who had undergone forensic assessment, which is known to incentivize malingering and symptom accentuation. Item selection yielded short versions of the scale with as few as 8 items (to differentiate between consistent participants and symptom producers) and as many as 10 items (to differentiate between consistent and inconsistent participants). The scales had higher classification accuracy than the original SIMS and did not show the bias that was originally reported between false positives and false negatives. Author Affiliation: (1) Department of Surgical, Medical Molecular & Critical Area Pathology, University of Pisa, Pisa, Italy (2) Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University, Chieti-Pescara, Italy (3) Department of General Psychology, University of Padova, Padova, Italy (4) Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy (b) Article History: Registration Date: 09/15/2020 Received Date: 05/04/2020 Accepted Date: 09/14/2020 Online Date: 10/13/2020 Byline:

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Gale Document Number: GALE|A658513184