Reducing Potential Risk Factors for Suicide-Related Behavior with a Group Intervention for Clients with Recurrent Suicide-Related Behavior
Bibliographic record
Abstract
BACKGROUND: This paper reports the results of a pilot study of a 20-week outpatient Psychosocial/Psychoeducational Intervention for Persons with Recurrent Suicide Attempts (PISA) targeting potential risk factors and areas of deficit, including cognitive, affective, and impulsivity known to characterize persons with recurrent suicide-related behavior. METHODS: One hundred sixty-three persons completed the 20-week intervention. Self-report questionnaires related to psychological deficits and risk factors associated with suicide-related behavior were given to participants before and after the intervention. Descriptive and paired t tests were performed using SPSS 15. RESULTS: This pilot study demonstrated that a 20-week group intervention led to significant pregroup and postgroup reductions in the risk factors associated with suicide-related behavior. Risk factors included cognitive factors: Beck Hopelessness Scale (P=.006), Satisfaction With Life Scale (P=.001), Problem Solving Inventory (P=.008); affective factors: Beck Depression Inventory (P=.018), Toronto Alexithymia Scale (P=.001); and impulsivity factors: Barratt Impulsivity Scale (P=.034). CONCLUSIONS: Findings in this pilot study suggest that participants in this short-term intervention report changes in identified risk factors and psychological deficits. These modest changes may be an important first step that facilitates persons at high-risk for suicide to access additional mental health services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".