Egosyntonic and Egodystonic: Accounting for Continuities and Discontinuities between Personality Traits and Personality Disorders
Bibliographic record
Abstract
BACKGROUND: Egosyntonic and egodystonic features describe differences between trait-based and state-based mental disorders. Many diagnoses, most particularly personality disorder (PD), show both features. These complex forms of psychopathology are an amalgam of traits and symptoms in which both egosyntonicity and egodystonicity can be present but vary in prominence. This distinction might help resolve the long-standing controversy as to whether PDs are best classified using dimensions or categories. METHOD: Narrative review. RESULTS: PDs in which egosyntonic features tend to predominate can be understood as amplified trait profiles. PDs associated with highly egodystonic clinical symptoms that more closely resemble major mental disorders may be usefully classified using both categories and dimensions. CONCLUSIONS: PDs with stronger egosyntonic features are more suitable for dimensional diagnosis. When egodystonic symptoms are more prominent, categorical diagnoses can be useful.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".