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Record W3216220182 · doi:10.1080/20008198.2021.1984048

The utility of the Structured Inventory of Malingered Symptomatology for distinguishing individuals with Dissociative Identity Disorder (DID) from DID simulators and healthy controls

2021· article· en· W3216220182 on OpenAlexaff
Bethany L. Brand, Michelle Barth, Yolanda R. Schlumpf, Hugo J. Schielke, Sima Chalavi, Eline M. Vissia, Ellert R. S. Nijenhuis, Lutz Jäncke, Antje A. T. S. Reinders

Bibliographic record

VenueEuropean journal of psychotraumatology · 2021
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsHomewood Research Institute
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekKing's College LondonNational Institute for Health and Care Research
KeywordsPsychologyClinical psychologyDissociativeMalingeringUnivariatePost-hoc analysisMultivariate analysisAnalysis of varianceDissociation (chemistry)PsychiatryMultivariate statisticsMedicineInternal medicine

Abstract

fetched live from OpenAlex

Background: Individuals with dissociative identity disorder (DID) have complex symptoms consistent with severe traumatic reactions. Clinicians and forensic assessors are challenged by distinguishing symptom exaggeration and feigning from genuine symptoms among these individuals. This task may be aided by administering validity measures. Objective: This study aimed to document how individuals with DID score on the Structured Inventory of Malingered Symptomatology (SIMS). The second objective was to compare coached DID simulators and healthy controls to DID patients on the SIMS's total score and subscales. The third objective was to examine the utility rates of the SIMS in distinguishing simulated DID from clinically diagnosed DID. Method: We compared SIMS data gathered from participants from two Dutch sites, one Swiss site and one U.S. site. Sixty-three DID patients were compared to 77 coached DID simulators and 64 healthy controls on the SIMS. A multivariate analysis compared the groups on the SIMS total scores and subscales, and post-hoc Games Howell tests and univariate ANOVAs examined differences between the groups. Utility statistics assessed the accuracy of the SIMS in distinguishing clinical from simulated DID. Results: DID simulators scored significantly higher than DID individuals and healthy controls on every SIMS subscale as well as the total score. The majority (85.7%) of the individuals with DID scored above the cut-off, which is typically interpreted as indicative of possible symptom exaggeration. DID individuals scored higher than the healthy controls on every subscale except Low Intelligence, even after controlling for dissociation. The subscales and items most frequently endorsed by the DID group are consistent with symptoms associated with complex trauma exposure and dissociative reactions. The SIMS total score had a sensitivity of 96% but an unacceptably low specificity of 14%. Conclusions: The findings indicate that the instrument is not accurate in assessing potential symptom exaggeration or feigning in DID.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.299
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2021
Admission routes1
Has abstractyes

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