Adolescent health outcomes: associations with child maltreatment and peer victimization
Bibliographic record
Abstract
Abstract Background Child maltreatment (CM) and peer victimization (PV) are serious issues affecting children and adolescents. Despite the interrelatedness of these exposures, few studies have investigated their co-occurrence and combined impact on health outcomes. The study objectives were to determine the overall and sex-specific prevalence of lifetime exposure to CM and past-month exposure to PV in adolescents, and the impact of CM and PV co-occurrence on non-suicidal self-injury, suicidality, mental health disorders, and physical health conditions. Methods Adolescents aged 14–17 years (n = 2,910) from the 2014 Ontario Child Health Study were included. CM included physical, sexual, and emotional abuse, physical neglect, and exposure to intimate partner violence. PV included school-based, cyber, and discriminatory victimization. Logistic regression was used to compare prevalence by sex, examine independent associations and interaction effects in sex-stratified models and in the entire sample, and cumulative effects in the entire sample. Results About 10% of the sample reported exposure to both CM and PV. Sex differences were as follows: females had increased odds of CM, self-injury, suicidality, and internalizing disorders, and males had greater odds of PV, externalizing disorders, and physical health conditions. Significant cumulative and interaction effects were found in the entire sample and interaction effects were found in sex-stratified models, indicating that the presence of both CM and PV magnifies the effect on self-injury and all suicide outcomes for females, and on suicidal ideation, suicide attempts, and mental health disorders for males. Conclusions Experiencing both CM and PV substantially increases the odds of poor health outcomes among adolescents, and moderating relationships affect females and males differently. Continued research is needed to develop effective prevention strategies and to examine protective factors that may mitigate these adverse health outcomes, including potential sex differences.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".