Bullying victimization among preadolescents in a community-based sample in Canada: a latent class analysis
Bibliographic record
Abstract
OBJECTIVE: Bullying victimization among adolescents has been well-recognized as a behavior associated with adverse psychological and mental health outcomes. Most studies on bullying victimization have focused on adolescents, but research is sparse regarding school victimization among preadolescents before they transition to adolescence. This study sought to identify latent classes of different types of co-occurring bullying victimization, based on a sample of 3829 school students in grades 5-8, ages 9-14 in the year of 2011 from the Saskatoon Health Region, Saskatchewan, Canada. RESULTS: Using a latent class analysis approach, the results uncovered three groups of victimized students, including those who were aggressively victimized (7.2%), moderately victimized (34.6%) and non-victimized (58.2%). Younger age and being overweight was associated with a higher likelihood of bullying victimization. Moderately and aggressively victimized students had greater probabilities of feeling like an outsider, experiencing anxiety, depressed moods, engaging in suicidal ideation and drinking when compared to non-victimized students. Peer and parent supports had significant protective effects against being victimized. Given the negative consequences of recurrent victimization among the preadolescents, it is imperative to address bullying incidents as they occur to prevent repeated transgressions, especially for those who suffer from multiple types of victimization.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".