Aggression, Suicidality, and Emotion Profiles in Youth: Links to Early Life Adversity
Bibliographic record
Abstract
Suicidality and physical aggression are leading, related youth public health concerns. Yet, whether adolescents who harm themselves, others, or both differ emotionally and etiologically remains unclear. To address this, adolescents from a prospective population-based birth cohort reported their suicidality, physical aggression, depression/anxiety symptoms, anger, and callousness (N = 1637). Distinct latent harm-emotion profiles were identified, which were linked to perinatal and childhood experiences. A six-profile solution was retained: Low harm (79.5%), moderate suicidality (6.5%), high suicidality (2%), high aggression (2.5%), moderate aggression (8.5%), and high suicidality and aggression (dual harm; 1%). Elevated harm profiles were compared to the low-harm group. Moderate/high suicidality profiles showed slight elevations in physical aggression. All elevated harm profiles expressed higher negative emotionality. Dual harm and aggression groups reported higher callousness, while suicidality groups reported lower callousness. Aggression profiles were 75% male, suicidality profiles were 21% male, while the low and dual-harm profiles were more similarly mixed sex (47% vs. 63% male, respectively). Low-harm youth experienced more positive childhood parenting. The dual harm and high aggression groups had more deviant childhood best friends, while the dual harm and moderate aggression groups had lower early life household income. The moderate suicidality group had fathers with higher depressive symptoms during infancy and childhood. Thus, one in five youth showed relatively elevated suicidality and/or physical aggression; of which, 95% tended to have a primary target (themselves or others). Early life economic, parental, and peer support may be key for preventing suicidal and aggressive outcomes in adolescence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".