MétaCan
Menu
Back to cohort
Record W1987312448 · doi:10.1037/a0020042

Further validation of the MMPI-2 and MMPI-2-RF Response Bias Scale: Findings from disability and criminal forensic settings.

2010· article· en· W1987312448 on OpenAlexaff
Dustin B. Wygant, Martin Sellbom, Roger O. Gervais, Yossef S. Ben‐Porath, Kathleen P. Stafford, David B. Freeman, Robert L. Heilbronner

Bibliographic record

VenuePsychological Assessment · 2010
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMinnesota Multiphasic Personality InventoryPsychologyResponse biasPersonalityPersonality testMalingeringClinical psychologyTest validityPsychometricsPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

The present study extends the validation of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) and the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF) Response Bias Scale (RBS; R. O. Gervais, Y. S. Ben-Porath, D. B. Wygant, & P. Green, 2007) in separate forensic samples composed of disability claimants and criminal defendants. Using cognitive symptom validity tests as response bias indicators, the RBS exhibited large effect sizes (Cohen's ds = 1.24 and 1.48) in detecting cognitive response bias in the disability and criminal forensic samples, respectively. The scale also added incremental prediction to the traditional MMPI-2 and the MMPI-2-RF overreporting validity scales in the disability sample and exhibited excellent specificity with acceptable sensitivity at cutoffs ranging from 90T to 120T. The results of this study indicate that the RBS can add uniquely to the existing MMPI-2 and MMPI-2-RF validity scales in detecting symptom exaggeration associated with cognitive response bias.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.106
GPT teacher head0.426
Teacher spread0.320 · 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 source (direct Gemma or distilled Codex), 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

Citations89
Published2010
Admission routes1
Has abstractyes

Explore more

Same venuePsychological AssessmentSame topicTraumatic Brain Injury ResearchFrench-language works237,207