Bibliographic record
Abstract
The treatment of patients with severe personality disorders, particularly those who meet criteria for the category of borderline personality (BPD), is well known to be difficult and marked by multiple crises. This chapter will focus on the most common scenario, which occurs when patients threaten to kill themselves or make a suicide attempt. Suicidal crises and suicide prevention Patients with severe personality disorders make multiple suicide attempts, gestures, and threats (Soloff et al ., 2000). These behaviors are often brought on by a breach in an intimate relationship (Gunderson, 2001). The most common scenario is an impulsive but non-lethal overdose, carried out in circumstances in which rescue is likely. Self-mutilation, particularly wrist-cutting, is also common in severe personality disorders (Gerson and Stanley, 2002). But this pattern should not always be considered as suicidal behavior. This behavior might have a different purpose from an overdose: instead of providing escape from a difficult situation, cutting functions as a means of regulating dysphoric affects (Brown et al ., 2002; Leibenluft et al ., 1987), and can take on some of the characteristics of an addiction (Linehan, 1993). Suicide attempts obviously require attention from therapists. However, it has never been shown that we can prevent patients with severe personality disorders from killing themselves. Moreover, it is very difficult to predict who is most at risk. In a population characterized by repeated suicide attempts, it is important to note that patients who make repeated attempts are statistically more at risk for completion (Zahl and Hawton, 2004).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".