Temperament as a Prospective Predictor of Self-Injury Among Patients With Borderline Personality Disorder
Bibliographic record
Abstract
This study examined the association of novelty seeking, harm avoidance, and reward dependence with different types (suicide attempts vs. nonsuicidal self-injury) and aspects (medical risk, impulsiveness, suicide intent) of self-injury over a 12-month period. Fifty-five female patients with borderline personality disorder enrolled in clinical trials completed Cloninger's Temperament and Character Inventory at pretreatment as well as the Suicide Attempt Self-Injury Interview at four-month intervals starting from the pretreatment assessment. Regression analyses indicated that the reward dependence subscale of attachment, a protective factor, was most consistently and uniquely associated with aspects of self-injury, including prestudy and prospective nonsuicidal self-injury and suicide intent, and prospective suicide attempts. After controlling for prestudy self-injury, few temperament variables predicted prospective self-injury. Higher scores on both the novelty seeking subscale of impulsiveness and the reward dependence attachment subscale were associated with lower prospective suicide intent even after controlling for pre-study suicide intent.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".