The impact of child maltreatment on mental health and substance use trajectories among adolescents
Bibliographic record
Abstract
AIM: There is robust evidence that child maltreatment is a significant risk factor and linked to negative psychological outcomes. However, few studies have examined the impact of child maltreatment on mental health and substance use trajectories across adolescence. METHODS: = 12.73, SD = 0.67, 49.7% female, 57.6% Caucasian/White). Multivariate multinomial logistic regressions were conducted to examine whether youth with maltreatment histories differed in their internalizing, externalizing, and substance use problems trajectories (based on previous studies) than youth without maltreatment histories. Moderation analyses using multinomial logistic regression were also conducted to examine perceived family support and school connectedness as protective factors against the impact of maltreatment. RESULTS: Youth who experienced maltreatment were more likely to display more severe internalizing, externalizing, and substance use problem trajectories than youth without such histories. While not significant as moderators, perceived family support and school connectedness were significantly associated with each of the trajectories, with lower levels of perceived family support and school connectedness linked to more severe problem trajectories. CONCLUSIONS: Results highlight the ongoing and significant harmful impact of maltreatment among youth. Results also support further prevention and intervention efforts for child maltreatment, particularly at the family and school level.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| 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".