Examining the pathways between bully victimization, depression,academic achievement, and problematic drinking in adolescence.
Bibliographic record
Abstract
In this article, we expand and test several theoretical models addressing the longitudinal relationships between bully victimization, depression, academic achievement, and problematic drinking from 3 approaches: Interpersonal risk model, symptom driven model, and a transactional model. Unfortunately, prior research has failed to consider these associations at the within-person level, which is arguably a more relevant level of analysis. Participants were 1,875 students sampled from four Midwestern middle schools and followed for 2 years. Baseline age ranged from 11-13 years with a racially diverse sample (44.3% African American, 29.2% White, 7% Hispanic, 3% Asian/Pacific Islander, and 16.5% Multi-Racial). The current study used an auto-regressive latent trajectory with structured residuals (ALT-SR) model to examine the within-person cross-lagged associations between bully victimization, depression, academic achievement, and problematic drinking. Results indicated support for an interpersonal risk model, where experiences of early bullying victimization resulted in a cascade of problems throughout middle school. Within this interpersonal risk model we also established that academic achievement was a key mechanism linking bully victimization to problematic drinking during adolescence We did not find evidence for a traditional symptom driven model (e.g., stemming from depression); however, we did find long-term problems stemming from early problematic drinking. Results are discussed in relation to prevention interventions for problematic drinking as well as screenings for early adolescent depression, bully victimization, and academic problems. (PsycINFO Database Record
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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 | 0.002 | 0.006 |
| 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) | 0.001 | 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".