The impact of a telephone hotline on suicide attempts and self-injurious behaviors in patients with borderline personality disorder
Bibliographic record
Abstract
Background Borderline personality disorder is often associated with self-injurious behaviors that cause personal suffering, family distress, and substantial medical costs. Mental health hotlines exist in many countries and have been shown to be effective in some contexts, but none have been specifically designed for borderline patients. The aim of the present study is to evaluate the impact of a 24/7 hotline dedicated to patients with borderline personality disorder on suicide attempts and self-injurious behaviors. Methods We conducted a single-blind, multicenter (9 French centers) clinical trial with stratified randomization (by age, sex and center). Patients (N = 315) with a diagnosis of borderline personality disorder (according to the SIDP-IV) were randomized into two groups with or without access to the hotline in addition to treatment as usual. The number of suicide attempts and self-injurious behaviors in each group within 12 month were analyzed in the “per protocol” population (Student’s t-tests, 5% significance threshold), adjusting for possible confounders in a multivariate analysis (using Poisson regression). The percentage of patients with suicide attempts and with self-injurious behaviors (and other percentages) were analyzed in the per protocol population (χ2-tests or exact Fischer tests, 5% significance threshold). Results The mean number of suicide attempts was 3 times lower in the hotline group (0.41 vs. 1.18, p = 0.005) and the mean number of self-injurious behaviors was 9 times lower (0.90 vs. 9.5, p = 0.006). Multivariate analysis confirmed the effectiveness of the hotline in reducing suicide attempts and self-harm. Conclusion This study supports the effectiveness of hotlines in reducing self-aggressive behavior in patients with borderline personality disorder. Such support is easy to use, cheap and flexible, and therefore easy to implement on a large scale.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".