The Suicide Status Form‐4 (<scp>SSF</scp>‐<scp>IV</scp>) as a potentially therapeutic suicide risk assessment tool
Bibliographic record
Abstract
BACKGROUND: Empirically supported suicide risk assessment and conceptualization is a central aim of the Zero Suicide model. The Suicide Status Form (SSF) is the essential document and scaffolding of the Collaborative Assessment and Management of Suicidality-Brief Intervention (CAMS-BI) and is hypothesized as an example of a psychological assessment as therapeutic intervention (PATI). However, this hypothesis has never been directly tested. METHODS: N = 57 patients deemed at risk for outpatient suicidal behavior and treated as part of an inpatient psychiatric consultation and liaison service were recruited to participate in CAMS-BI at a Level 1 trauma center in the southeastern United States. During the CAMS-BI process, patients were asked to rate their subjective units of distress (SUDS) at five time points throughout the intervention (k = 285). RESULTS: The omnibus random intercept multilevel model revealed a significant difference in pre- to post-session ratings of SUDS across patients. Post hoc pairwise comparisons revealed no significant differences between SSF sections (e.g., Section A, Section B, and Section C) and relative reductions in SUDS; however, there was an observable trend toward a favorable effect of Section A of the SSF. CONCLUSIONS: The SSF may represent an example of PATI pending replication and extension of the current results.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".