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Record W2618011121 · doi:10.1037/pas0000507

Development and validation of an Overreporting Scale for the Personality Inventory for DSM–5 (PID-5).

2017· article· en· W2618011121 on OpenAlexaff
Martin Sellbom, Sonya Dhillon, R. Michael Bagby

Bibliographic record

VenuePsychological Assessment · 2017
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMinnesota Multiphasic Personality InventoryPsychologyPsycINFOReliability (semiconductor)PsychometricsClinical psychologyPersonalityTest validityScale (ratio)Personality Assessment InventoryConcurrent validityDSM-5Personality disordersValidityPersonality testSocial psychologyMEDLINE

Abstract

fetched live from OpenAlex

(PID-5) to detect noncredible overreported responding. To this end, we used a rare symptoms approach and identified extreme response options on PID-5 items that were infrequently endorsed by students in 3 different university samples (N = 1,370) and in a psychiatric patient sample (N = 194). The resulting 10-item scale (the PID-5-ORS) produced adequate-to-good estimates of internal reliability and was significantly correlated with the Minnesota Multiphasic Personality Inventory-2 Restructued Form (MMPI-2-RF) overreporting validity scales, providing evidence of concurrent validity. The criterion validity of the PID-5-ORS was demonstrated in an analog simulation design study. More specifically, university students instructed to overreport (n = 80) scored substantially higher on the PID-5-ORS relative to both a group of genuine psychiatric patients and students instructed to complete the PID-5 under standard (honest) instructions (n = 161); the effect size magnitudes associated with these differences were large. Classification accuracy analyses further revealed that high scores on the PID-5-ORS were associated with high specificity (and thus, low rates of false positive classifications) in differentiating overreporters from genuine patients, with sensitivity being somewhat weaker. (PsycINFO Database Record

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.151
GPT teacher head0.469
Teacher spread0.317 · 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

Citations25
Published2017
Admission routes1
Has abstractyes

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