Coupling of Temperament with Mental Illness in Four Age Groups
Bibliographic record
Abstract
Studies of temperament profiles in patients with mental disorders mostly focus on emotionality-related traits, although mental illness symptoms include emotional and nonemotional aspects of behavioral regulation. This study investigates relationships between 12 temperament traits (9 nonemotionality and 3 emotionality related) measured by the Structure of Temperament Questionnaire and four groups of clinical symptoms (depression, anxiety, antisociality, and dominance-mania) measured by the Personality Assessment Inventory. The study further examines age differences in relationships among clinical symptoms and temperament traits. Intake records of 335 outpatients and clients divided into four age groups (18-25, 26-45, 46-65, and 66-85) showed no significant age differences on depression scales; however, the youngest group had significantly higher scores on Anxiety, Antisocial Behavior, Dominance, and Thought Disorders scales. Correlations between Personality Assessment Inventory and Structure of Temperament Questionnaire scales were consistent with Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, descriptors showing strong concurrent validity. Several age differences on temperament scales are also reported. Results show the benefits of differentiation between physical, social-verbal, and mental aspects of activities, as well as differentiation between dynamical, orientational, and energetic aspects in studying mental illness and temperament.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".