Suicidal Risk and Affective Temperaments, Evaluated with the TEMPS-A Scale: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Among risk factors for suicidal behavior, there is growing interest in associations with stable affective temperament types, particularly based on assessment with the TEMPS-A self-rating scale. AIM: As research on this topic has not been reviewed systematically, we synthesized relevant, reported research findings. METHODS: Systematic searching identified peer-reviewed reports pertaining to associations of suicidal behavior or ideation with affective temperament types evaluated with TEMPS-A. We summarized available findings and applied quantitative meta-analytic methods to compare scale scores in suicidal versus nonsuicidal subjects. RESULTS: In 21 of 23 TEMPS-A studies meeting inclusion criteria, anxious, cyclothymic, depressive, or irritable temperament scores were significantly higher with previous or recent suicide attempts or ideation in both psychiatric and general population samples compared to nonsuicidal controls, whereas hyperthymic temperament scores were lower in 9 of 11 reports. These findings were synthesized by random-effects meta-analyses of standardized mean differences in TEMPS-A temperament scores in suicidal versus nonsuicidal subjects. Associations ranked: depressive ≥ irritable > cyclothymic > anxious > hyperthymic (negative). CONCLUSIONS: Affective temperaments, especially depressive and irritable, were strongly associated with suicidal risk, whereas hyperthymic temperament appeared to be protective.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".