A Description of a Psychosocial/Psychoeducational Intervention for Persons with Recurrent Suicide Attempts
Bibliographic record
Abstract
This paper gives a description of a psychosocial/psychoeducational group intervention for individuals with a history of recurrent suicide attempts. The intervention was conceived to reduce the risk of future suicidal behavior and to modify the client's psychopathology. Three features are felt to make the intervention unique from others described in the literature. First, the intervention is targeted at both men and women from an inner-city population who are often underhoused, underemployed, and undereducated. 24 of 48 clients (50%) lived alone, and 24 of those (92%) were living in subsidized housing; 33% lived in supportive housing, and one lived on the street at the time of assessment. 48% had a high-school education or less. Second, the principles of our approach stressed client validation and participation in the development and delivery of the therapy. Our frame of reference was to name ourselves as professionals with a set of skills and access to some kinds of information and clients as the experts on the experience in their lives. Third, the group content incorporated a multimodal approach to meet the varied needs of the clients. Future reports will discuss the empirical evaluation of this intervention; however, the development of specific, targeted approaches for unique individuals with recurrent suicide attempts is clearly needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".