Risk factors for suicide attempt in children, adolescents, and young adults hospitalized for mental health disorders
Bibliographic record
Abstract
BACKGROUND: Suicide is a leading cause of death among children, adolescents, and young adults (AYA), and mental health disorders are a major contributing factor. Yet, suicidal behaviors among children and AYA with mental health concerns remain understudied and age-specific risk factors are poorly understood. We examined the risk factors for suicide attempt in children and AYA with mental health disorders across three age groups: pre-adolescent children (aged ≤ 12), adolescents (aged 13-17), and young adults (aged 18-25). METHODS: A cross-sectional study of children and AYA hospitalized for a mental health disorder (n = 18,018) at a private hospital system with 141 facilities across the United States (year 2014). RESULTS: Suicide attempts six months prior to hospitalization were reported in 12.1% (n = 177) pre-adolescent children, 22% (n = 1476) adolescents, and 17.9% (n = 1766) young adults. Evidence of psychological trauma was present in 55.4% of pre-adolescent children, 51.2% of adolescents, and 44.5% of young adults. Predictors for suicide attempt observed across all three age groups included the following: female sex, depressive disorder, and being a victim of bullying. Risk factors for suicide attempt specific to pre-adolescent children included being uninsured and having an unsafe home or school environment. Among AYA, suicide attempt was associated with non-Hispanic white, family history of suicide, emotional traumas, and other traumatic experiences. Alcohol use disorder was also a significant predictor of suicide attempt in young adults. CONCLUSIONS: Suicide attempts among children and AYA admitted to a hospital with mental health concerns are highly prevalent. Socioeconomic stressors appeared to be an important contributing factor of suicidal behavior in pre-adolescent children but not in older AYA. Effective suicide prevention strategies targeting children and AYA would need to consider age-specific risk factors.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".