Predicting borderline personality disorder symptoms in adolescents from childhood physical and relational aggression, depression, and attention-deficit/hyperactivity disorder
Bibliographic record
Abstract
Developmental cascade models linking childhood physical and relational aggression with symptoms of depression and attention-deficit/hyperactivity disorder (ADHD; assessed at ages 10, 11, 12, 13, and 14) to borderline personality disorder (BPD) features (assessed at age 14) were examined in a community sample of 484 youth. Results indicated that, when controlling for within-time covariance and across-time stability in the examination of cross-lagged relations among study variables, BPD features at age 14 were predicted by childhood relational aggression and symptoms of depression for boys, and physical and relational aggression, symptoms of depression, and symptoms of ADHD for girls. Moreover, for boys BPD features were predicted from age 10 ADHD through age 12 depression, whereas for girls the pathway to elevated BPD features at age 14 was from depression at age 10 through physical aggression symptoms at age 12. Controlling for earlier associations among variables, we found that for girls the strongest predictor of BPD features at age 14 was physical aggression, whereas for boys all the risk indicators shared a similar predictive impact. This study adds to the growing literature showing that physical and relational aggression ought to be considered when examining early precursors of BPD features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".