Individual-level factors related to better mental health outcomes following child maltreatment among adolescents
Bibliographic record
Abstract
Research on factors associated with good mental health following child maltreatment is often based on unrepresentative adult samples. To address these limitations, the current study investigated the relationship between individual-level factors and overall mental health status among adolescents with and without a history of maltreatment in a representative sample. The objectives of the present study were to: 1) compute the prevalence of mental health indicators by child maltreatment types, 2) estimate the prevalence of overall good, moderate, and poor mental health by child maltreatment types; and 3) examine the relationship between individual-level factors and overall mental health status of adolescents with and without a history of maltreatment. Data were from the National Comorbidity Survey of Adolescents (NCS-A; n = 10,123; data collection 2001-2004); a large, cross-sectional, nationally representative sample of adolescents aged 13-17 years from the United States. All types of child maltreatment were significantly associated with increased odds of having poor mental health (adjusted odds ratios ranged from 3.2 to 9.5). The individual-level factors significantly associated with increased odds of good mental health status included: being physically active in the winter; utilizing positive coping strategies; having positive self-esteem; and internal locus of control (adjusted odds ratios ranged from 1.7 to 38.2). Interventions targeted to adolescents with a history of child maltreatment may want to test for the efficacy of the factors identified above.
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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 | 0.000 |
| 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".