Impulsive Phenomena, the Impulsive Character (der Triebhafte Charakter) and DSM Personality Disorders
Bibliographic record
Abstract
Impulsive phenomena have frequently been associated with personality disorders, beginning with Reich's description of the impulsive-character (Reich, 1925/1975). However, questions remain regarding the cooccurrence of a wide variety of impulsive phenomena and whether an underlying structure influences the differential association of impulses to individual personality disorders. Adults entering residential treatment for treatment-refractory disorders were interviewed about their lifetime histories of 33 impulse items, following independent diagnostic interviews. Factor analysis suggested 12 underlying dimensions of impulsive phenomena, explaining 68% of the variance. Borderline and antisocial PDs had the highest impulse scores, followed by self-defeating, narcissistic, depressive, and passive-aggressive PDs. Schizoid, avoidant, obsessive-compulsive, and dependent types were negatively associated with impulsive phenomena. Individuals with the highest impulse scores showed higher levels of borderline, antisocial and either self-defeating or passive-aggressive personality pathology, and were characterized by high Neuroticism and Openness and low Agreeableness on the NEO-FFI. Personality disorders and the NEO-FFI personality traits both predicted unique variance in impulsive phenomena, with the former predominating. Our findings bear striking similarities to Reich's (1925/1975) descriptions of the impulsive character.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".