Coping Motives as Moderators of the Relationship Between Emotional Distress and Alcohol Problems in a Sample of Adolescents Involved with Child Welfare
Bibliographic record
Abstract
Adolescents involved with child welfare are at particular risk for the development of alcohol problems due to their histories of child maltreatment. Based on an emotion dysregulation-maladaptive coping model of alcohol problems, the current study explored whether drinking to cope with negative affect (coping motives) moderated the relationship between emotional distress (i.e., anxiety and depression symptoms) and alcohol problems in a sample of adolescents involved with child welfare. Participants were 202 adolescents (54.4% females, ages 14–17) from the Maltreatment and Adolescent Pathways (MAP) Longitudinal Study who completed measures of childhood maltreatment, alcohol use and alcohol problems, drinking motives, and symptoms of anxiety and depression. Controlling for gender, age, alcohol use, and child maltreatment, coping motives were signifi cantly associated with alcohol problems, and the Anxiety × Coping and Depression × Coping interaction were also signifi cant. Increased anxiety symptoms were associated with more alcohol problems for adolescents with high coping motives, whereas increased depression symptoms were associated with fewer alcohol problems among those with high coping motives. We discuss the implications of these fi ndings for the development of interventions addressing anxiety in the context of using alcohol to cope among adolescents involved with child welfare.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".