Changes in suicide and non‐suicidal self‐injury ideation and the moderating role of specific emotions over the course of dialectical behavior therapy
Bibliographic record
Abstract
OBJECTIVE: Dialectical behavior therapy (DBT) targets suicidal behavior and non-suicidal self-injury (NSSI) as well as urges/ideation to engage in these behaviors. However, it remains unclear which specific suicidal ideation (SI) and NSSI ideation domains (i.e., frequency, worst intensity, average intensity, perceived likelihood of future ideation, and duration of ideation), if any, are impacted, and whether specific emotions moderate these effects. METHOD: 73 individuals with borderline personality disorder (BPD), enrolled in six months of DBT, completed interviews of suicide and NSSI ideation and self-report measures of specific emotions at baseline, mid-treatment, and post-treatment. RESULTS: Generalized estimation equations revealed that all domains of suicidal ideation decreased over the course of DBT, but for NSSI domains, only ideation intensity decreased. Higher levels of shame/guilt predicted less, and higher fear predicted more, reduction in SI and NSSI ideation frequency. Higher shame/guilt also predicted more reduction in worst SI intensity. Higher sadness predicted greater reductions in SI intensity and duration, but less reductions in the perceived likelihood of future NSSI ideation. CONCLUSIONS: These findings suggest that DBT effectively reduces several facets of SI, but more work is required to target NSSI ideation. Results also suggest that targeting shame/guilt may be important to reducing SI and NSSI ideation.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".