Examining Risk Factors for Suicidality in Adolescents and Adults Experiencing Their First Episode of Psychosis
Bibliographic record
Abstract
This narrative review aimed to identify the risk factors associated with suicidality in adolescents and adults with first-episode psychosis. The review included studies that examined various factors such as psychiatric, familial, and social factors, as well as previous self-harm, suicidal ideation, and comorbid mental health disorders. A comprehensive literature search was conducted across three publicly available databases (Embase, American Psychological Association PsycINFO, and PubMed) using specific search terms related to first-episode psychosis, suicide, self-harm, and children/adolescents and adults. The inclusion criteria included original articles focusing on prospective and retrospective cohort trials, with substantial data on first-episode psychosis and self-harm, measuring both suicidal intent and outcome. Non-original studies, case reports, case series, non-English-language publications, and studies examining violence and self-harm related to substance-induced psychosis were excluded. After manual screening and removing duplicate articles, 13 articles met the established criteria for inclusion in this review. Included studies adhered to similar inclusion and exclusion criteria, had long-term follow-up, and assessed outcomes at least twice. The findings suggest that depressive symptoms, substance use disorders, previous self-harm or suicidal ideation, and longer duration of untreated psychosis are associated with an increased risk of suicidality. However, insights into psychosis and premorbid intellectual functioning did not show a direct association with suicidality.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".