Investigating the Clinical Profile of Suicide Attempters Who Used a Violent Suicidal Means
Bibliographic record
Abstract
In our study, we aimed to explore the profile of the high-risk subgroup of suicide attempters that used a violent means compared to suicide attempters that chose a non-violent suicide means. Therefore, we recruited a sample of inpatients with recent suicide attempts in three psychiatric hospitals in Thuringia, Germany. We used a structured clinical interview to assess the psychiatric diagnoses, sociodemographic data, and characteristics of the suicide attempt. Furthermore, we used several validated clinical questionnaires to measure suicidal ideations, suicide intent, depression severity, hopelessness, impulsivity, aggression, anger expression, and childhood trauma. We compared 41 individuals using violent means to 59 using non-violent means with univariate and multivariate statistical analyses. We found significantly (corrected for multiple comparisons) higher levels of impulsivity-related sensation-seeking in violent suicide attempters in univariate and multivariate analyses, and additionally in anger expression directed inward at an uncorrected statistical threshold. Besides that, there were no significant differences between the two groups. We assume that underlying neurocognitive mechanisms, such as impaired decision-making processes and/or differences in risk/loss assessment, could explain the higher levels of questionnaire-based sensation-seeking in subjects who use violent suicide means. Further research is needed, including neuroimaging and biochemical techniques, to gain more insight into the mechanisms underlying the choice of a suicidal means.
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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 | 0.001 |
| 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".