Student Engagement as a Mediator Process Between Peer Victimization and Achievement at the Beginning of Middle School
Bibliographic record
Abstract
BACKGROUND: Very few studies have properly identified how peer victimization is associated with lower achievement in middle or high school. In this context, this study examined how peer victimization at the beginning of middle school is linked with subsequent student achievement. Specifically, it assessed if the behavioral, affective, and cognitive dimensions of engagement in school play a mediation role in the relationship between peer victimization and student achievement. METHODS: The sample of this study included 683 seventh graders attending 3 schools in Montreal, Canada. Students self-reported peer victimization at the beginning and end of grade 7. They also reported their levels of student engagement on the 3 dimensions (behavioral, affective, and cognitive) across 3 time points in seventh and eighth grades. Student achievement in language arts across these 2 years was also obtained through school records. RESULTS: Peer victimization significantly predicted lower achievement over time (b = -.24, p ≤ .001). Peer victimization predicted lower achievement in grade 8 indirectly through affective student engagement (b = -.11, p < .05). Post hoc analyses showed that peer victimization still predicted lower achievement in grade 8 indirectly through a decrease in affective engagement (b = -.14, p < .05). However, when considered alone, a decrease in cognitive engagement also acted as a mediator (b = -.09, p < .05), suggesting a strong link with affective engagement. CONCLUSION: Our findings expose the importance to promote student engagement in school and achievement for victimized youth.
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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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".