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Record W2083989218 · doi:10.1080/13854046.2011.585141

MMPI-2 Restructured Form Over-Reporting Scales in First-Episode Psychosis

2011· article· en· W2083989218 on OpenAlexaff
Scot E. Purdon, Stacey M. Purser, Kim M. Goddard

Bibliographic record

VenueThe Clinical Neuropsychologist · 2011
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsAlberta Hospital EdmontonUniversity of AlbertaAlberta Health Services
Fundersnot available
KeywordsMinnesota Multiphasic Personality InventoryPsychosisPsychopathologyClinical psychologyPsychiatryPsychologyCognitionThought disorderSchizophrenia (object-oriented programming)MedicinePersonality

Abstract

fetched live from OpenAlex

MMPI-2-RF over-reporting scales for physical, cognitive, or psychological symptoms were examined in 130 consecutive referrals to a first-episode psychosis (FEP) clinic. Although acutely ill upon presentation, consistent and responsive profiles were obtained in 79% of the sample. There was no indication of under-reporting on defensive scales, and anticipated elevations were observed on clinical scales sensitive to thought disorder, ideas of persecution, and aberrant experiences. The Infrequent Somatic (Fs), Symptom Validity Scale (FBS-r), and Response Bias (RBS) scales did not indicate somatic or cognitive over-reporting, but the Infrequent Psychopathology Scale (Fp-r) showed a moderate elevation that may suggest a propensity for over-reporting or an effect of clinical symptoms on the over-reporting scale. Clinician ratings of positive symptoms of psychosis were related to the Fp-r. Although the over-reporting classifications with the RBS were relatively low, RBS scores were directly related to positive and general symptoms of psychosis. The MMPI-2-RF appears to have clinical value in an acutely ill FEP sample. The sample was not prone to over-reporting pathology, but associations between both the Fp-r and the RBS with clinical symptoms will warrant further investigation.

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)0.002
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 score0.046
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.155
GPT teacher head0.415
Teacher spread0.260 · 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

Citations6
Published2011
Admission routes1
Has abstractyes

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