Temperament and Mood Disorders: Functional Ensemble of Temperament Perspective
Bibliographic record
Abstract
This presentation discusses links between mood disorders and temperament traits. The assessment of temperament utilized the Compact version of STQ (STQ-77) based on Trofimova's Functional Ensemble of Temperament model. The 12 temperament scales of the STQ-77 include 3 emotionality scales (Neuroticism, Impulsivity, Self-Confidence) and 9 scales measuring dynamic aspects of activity (endurance, programming-integration and orientation) analysed separately for physical, social and intellectual activities. Emotionality is presented in this model as an amplifier of the arousal, lability and orientation aspects of activity. The point of discussion will focus on the contribution of biological and temperament factors to mood disorders. The study is based on the administration of the Compact STQ-77 to156 healthy Canadian subjects and to clinical samples of 307 patients diagnosed with anxiety and depressive disorders. Significant correlations were found between 9 temperament traits and the presence of depression and between 3 temperament traits and the presence of anxiety. Putative links between the presence of anxiety and depression and dysfunction in mu and kappa opiod receptor protein systems will be discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".