Detecting malingering mental illness in forensics: Known-Group Comparison and Simulation Design with MMPI-2, SIMS and NIM

Machine learning item selection for short scale construction: A proof-of-concept using the SIMS

ObjectiveThis proof-of-concept paper provides evidence to support machine learning (ML) as a valid alternative to traditional psychometric techniques in the development of short forms of longer parent psychological tests. ML comprises a variety of feature selection techniques that can be efficiently applied to identify the set of items that best replicates the characteristics of the original test. MethodsIn the present study, we integrated a dataset of 329 participants from published and unpublished datasets used in previous research on the Structured Inventory of Malingered Symptomatology (SIMS) to develop a short version of the scale. The SIMS is a multi-axial self-report questionnaire and a highly efficient psychometric measure of symptom validity, which is frequently applied in forensic settings. Results State-of-the-art ML item selection techniques achieved a 72% reduction in length while capturing 92% of the variance of the original SIMS. The new SIMS short form now consists of 21 items. ConclusionsThe results suggest that the proposed ML-based item selection technique represents a promising alternative to standard psychometric correlation-based methods (i.e. item selection, item response theory), especially when selection techniques (e.g. wrapper) are employed that evaluate global, rather than local, item value.

Malingering of Psychotic Symptoms in Psychiatric Settings: Theoretical Aspects and Clinical Considerations

Malingering is the intentional production of false or grossly exaggerated physical or psychological symptoms motivated by external incentives. Although the Diagnostic and Statistical Manual of Mental Disorders (DSM–5) does not list malingering in its diagnostic section and therefore does not identify it as a formal mental disorder, malingering and verified mental illness commonly coexist. Some subtypes of feigning behaviors, such as partial or pure malingering, dissimulation, and false imputation, can be suspected when patients have marked discrepancies between reported stressors and objective findings. The article discusses these three theoretical concepts with their possible clinical aspects, illustrating each phenomenon by clinical case with self-reported and/or observed psychotic symptoms. We summarized relevant findings and provided a review of clinical considerations that physicians can use to aid in the evaluation of psychotic symptoms in the context of those three concepts.

[Response distortion or symptom severity? Symptom description by psychiatric patients and sociomedical assessment subjects]

BACKGROUND

Psychiatric disorders are the most frequent cause for premature retirement; however, trying to verify claims of performance deficits due to psychiatric disorders without the actual existence of the latter in sociomedical assessments is problematic. For this reason, differentiation between actual psychiatric disorders with real presence of symptoms and simulated or aggravated symptoms is of importance in sociomedical assessments. In recent years, symptom validity tests (SVT) have been increasingly utilized in psychiatric/psychological assessments; however, knowledge of the validity of these tests and the relation to symptom severity is still lacking.

OBJECTIVE

This study aimed to compare the validity of different SVTs depending on symptom severity (Structured Inventory of Malingered Symptomatology (SIMS), Word Memory Test (WMT), Symptom Checklist-90-R (SCL-90‑R), Minnesota Multiphasic Personality Inventory‑2 (MMPI-2)).

MATERIAL AND METHODS

Clinically treated inpatient psychiatric patients (n = 30) were compared with subjects of sociomedical assessments (n = 29) regarding their tendency to simulate or aggravate symptoms.

RESULTS

It could be shown that the WMT and the SIMS both failed in the differentiation between psychiatric patients and subjects of sociomedical assessments, regarding description of symptoms or (un)restricted performance motivation. Furthermore, 20% of psychiatric patients were classified as false positive in WMT. The results of the SIMS were significantly related to the severity of psychiatric symptoms in SCL-90‑R, therefore the severity of symptoms was assessed rather than the response distortion.

CONCLUSION

The results underline the importance of further research on SVTs in sociomedical assessments, especially regarding symptom severity and response distortion.

Detecting feigned symptoms of depression, anxiety, and ADHD, in college students with the structured inventory of malingered symptomatology

Objective: Research consistently shows how easily students can feign symptoms of ADHD on self-report checklists to determine eligibility for curricular and standardized testing accommodations. However, it is unclear how easily students can feign psychological symptoms to accesses academic accommodations, making the assessment of symptom validity important in both populations.Method: Using a between-subjects design, 75 college students were randomly assigned to one of three groups: (1) coached feigning of ADHD, (2) coached feigning of depression and anxiety (DA), and (3) honest responding (HR). Participants completed the Depression, Anxiety, and Stress Scale (DASS-21) and the Structured Inventory of Malingered Symptomatology (SIMS).Results: The SIMS showed 100% specificity, but low sensitivity (36-52%) for detecting feigned symptoms with different cutoffs. Differences on SIMS subtests were apparent by group with elevated scores for the DA group on the Affective Disorders subscale and elevation for the ADHD group on the Low Intelligence and Amnestic subscales. Participants identified as feigning by the SIMS typically reported more severe symptoms than participants not identified on the DASS-21.Conclusions: The SIMS equally classified the feigned ADHD and DA participants for both cutoff scores utilized. Potential reasons for low sensitivity rates are discussed and future research recommendations are made.