Differentiating normal and disordered personality using the General Assessment of Personality Disorder (GAPD)
Bibliographic record
Abstract
Criteria to differentiate personality disorder from extremes of normal personality variations are important given growing interest in dimensional classification because an extreme level of a personality dimension does not necessarily indicate disorder. The DSM-5 proposed classification of personality disorder offers a definition of general personality disorder based on chronic interpersonal and self/identity pathology. The ability of this approach to differentiate personality disorder from other mental disorders was evaluated using a self-report questionnaire, the General Assessment of Personality Disorder (GAPD). This measure was administered to a sample of psychiatric patients (N = 149) from different clinical sub-sites. Patients were divided into personality disordered and non-personality disordered groups on the basis of the Structured Clinical Interview for DSM-IV Axis II Disorders (SCID-II). The results showed a hit rate of 82% correct identified patients and a good accuracy of the predicted model. There was a substantial agreement between SCID-II interview and GAPD personality disorder diagnoses. The GAPD appears to predict personality disorder in general, which provides support of the DSM-5 general diagnostic criteria of personality disorder.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".