Indicators to distinguish symptom accentuators from symptom producers in individuals with a diagnosed adjustment disorder: A pilot study on inconsistency subtypes using SIMS and MMPI-2-RF

Abstract

In the context of legal damage evaluations, evaluees may exaggerate or simulate symptoms in an attempt to obtain greater economic compensation. To date, practitioners and researchers have focused on detecting malingering behavior as an exclusively unitary construct. However, we argue that there are two types of inconsistent behavior that speak to possible malingering—accentuating (i.e., exaggerating symptoms that are actually experienced) and simulating (i.e., fabricating symptoms entirely)—each with its own unique attributes; thus, it is necessary to distinguish between them. The aim of the present study was to identify objective indicators to differentiate symptom accentuators from symptom producers and consistent participants. We analyzed the Structured Inventory of Malingered Symptomatology scales and the Minnesota Multiphasic Personality Inventory-2 Restructured Form validity scales of 132 individuals with a diagnosed adjustment disorder with mixed anxiety and depressed mood who had undergone assessment for psychiatric/psychological damage. The results indicated that the SIMS Total Score, Neurologic Impairment and Low Intelligence scales and the MMPI-2-RF Infrequent Responses (F-r) and Response Bias (RBS) scales successfully discriminated among symptom accentuators, symptom producers, and consistent participants. Machine learning analysis was used to identify the most efficient parameter for classifying these three groups, recognizing the SIMS Total Score as the best indicator.

Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models

“…Recently, researchers in different scientific fields, including the clinical and social sciences, have emphasized the utility of focusing on prediction, rather than explanation, during data analysis [ 69 , 70 , 71 , 72 ]. This increased attention to predictive models may be largely attributed to the significant spread of machine learning (ML)—a branch of artificial intelligence that trains algorithms on data samples (i.e., training sets) in order to make predictions on completely new data (i.e., test sets) without being explicitly programmed to do so [ 73 ].…”

How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms

“…ML algorithms automatically learn information from a set of data and make predictions on unseen data without being explicitly programmed to do so. ML techniques have been shown to be particularly useful in predicting human behavior, including high-risk behavior [ 48 , 49 , 50 , 51 ]. Indeed, one of the main advantages of ML is that it enables inferences to be made at the individual level, whereas traditional statistical methods focus primarily on the group level [ 52 ].…”

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

“…In the present study, we extended the results reported by Mazza et al (2019c) (Bond, & Fox, 2015) treat selected items as local estimators of individual features. In classic psychometrics, linear correlation is usually the base for estimating item relevance in group discrimination tasks.…”

“…Malingering is the dishonest and intentional production or exaggeration of physical or psychological symptoms in order to obtain external gain (Tracy & Rix, 2017). Although malingering is coded in both the ICD-11 (World Health Organization, 2019) and the DSM-5 (American Psychiatric Association, 2013), it is not a binary "present" or "absent" phenomenon: it may exist in specific domains (e.g., psychological, cognitive, and medical domains), it is often comorbid with formal disorders (Mazza et al, 2019c;Rogers & Bender, 2018), and it can be classified into several types (Akca et al, 2020;Lipman, 1962;Resnick, 1997). Due to the considerable variation produced by these nuances, it is difficult to measure the prevalence of malingering in clinical and forensic populations.…”

“…Recently, Mazza et al (2019c) employed ML in an attempt to identify a strategy to distinguish between symptom accentuators, symptom producers, and consistent (i.e., truth telling) participants. Neglecting the diversity of malingering expressions, the researchers mostly considered it a unitary construct, both theoretically and empirically, assimilating aspects of both symptom production and symptom accentuation.…”