Developmental joint trajectories of anxiety-depressive trait and trait-aggression: implications for co-occurrence of internalizing and externalizing problems
Bibliographic record
Abstract
BACKGROUND: In youth, anxiety-depressive traits (ADT) and trait-aggression (TA) are important risk factors of exhibiting maladaptive behaviors in adulthood (i.e. violence and substance use). However, the developmental co-occurrence of these traits in youth remains unknown. We thus sought to investigate the developmental trajectories of ADT and TA within a data-driven approach. The aim was two-fold: (i) to examine the developmental trajectories of ADT and TA in youth from ages 10 to 16, and (ii) to investigate both childhood predictors and problematic outcomes of the identified joint trajectories. METHOD: The sample comprised 1354 children provided from the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN) Consortium. Group-based trajectory modeling was first employed to identify individual trajectory models of ADT and TA independently (from ages 10 to 16). Then, joint trajectory models were built on the found trajectories. Last, logistic regressions were used to evaluate the childhood characteristics and negative outcomes of the joint trajectory groups. RESULTS: Our results showed five trajectory groups with varying levels of ADT and TA. A significant co-occurrence between ADT and TA was found in three of the trajectory groups. Notably, higher levels of childhood psychopathology and more severe/frequent childhood abuse were found in the groups with moderate to high ADT and high TA. The groups with higher ADT and high TA were also more likely to exhibit violence and substance use. CONCLUSIONS: This study exposes the importance of assessing ADT and TA simultaneously and early in childhood to prevent and manage the risk of problematic behaviors 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".