A call to action: informing research and practice in suicide prevention among individuals with psychosis
Bibliographic record
Abstract
Although it is well established that individuals living with psychosis are at increased risk for suicidal ideation, attempts, and death by suicide, several gaps in the literature need to be addressed to advance research and improve clinical practice. This Call-to-Action highlights three major gaps in our understanding of the intersection of psychosis and suicide as determined by expert consensus. The three gaps include research methods, suicide risk screening and assessment tools used with persons with psychosis, and psychosocial interventions and therapies. Specific action steps to address these gaps are outlined to inform research and practice, and thus, improve care and prognoses among persons with psychosis at risk for suicide.
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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 | 0.408 | 0.415 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.025 | 0.057 |
| Open science | 0.012 | 0.030 |
| Research integrity | 0.054 | 0.062 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".