Relationship between personality disorder symptoms and temperament in the young male general population of South Korea
Bibliographic record
Abstract
The aim of the present study was to identify the characteristics of temperament and character in personality disorder symptoms in the young male general population. A total of 585 male subjects from the same community were included in the study (mean age, 19.06 +/- 0.26 years). There was no difference in socioeconomic and educational background. Subjects completed the Personality Disorder Questionnaire-IV+ (PDQ-IV+) and Temperament and Character Inventory (TCI). There were unique correlations between each personality disorder symptom and four temperament profiles. When classification was done through three cluster symptoms by DSM-IV, cluster A symptoms were most strongly associated with low reward dependence (r = -0.46), cluster B with high novelty seeking (r = 0.33), and cluster C with high harm avoidance (r = 0.47). The character dimension, self-directedness was the most powerful predictor of the presence of any personality disorders. In homogenous male general population, unique combinations were found between temperament and each personality disorders. Although the subjects were relatively young and therefore their characters had not yet fully matured, character played an important role in the presence of personality disorder. Temperament can be used to differentiate the personality symptoms and characters used to predict the presence 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".