Cybervictimization in adolescence and its association with subsequent suicidal ideation/attempt beyond face‐to‐face victimization: a longitudinal population‐based study
Bibliographic record
Abstract
BACKGROUND: Cross-sectional associations have been documented between cybervictimization and suicidal risk; however, prospective associations remain unclear. METHODS: Participants were members of the Quebec Longitudinal Study of Child Development (QLSCD), a prospective birth cohort of 2,120 individuals followed from birth (1997/98) to age 17 years (2014/15). Cybervictimization and face-to-face victimization experienced since the beginning of the school year, as well as serious suicidal ideation and/or suicide attempt were self-reported at ages 13, 15 and 17 years. RESULTS: In cross-sectional analyses at 13, 15 and 17 years, adolescents cybervictimized at least once had, respectively, 2.3 (95% CI = 1.64-3.19), 4.2 (95% CI = 3.27-5.41) and 3.5 (95% CI = 2.57-4.66) higher odds of suicidal ideation/attempt after adjusting for confounders including face-to-face victimization, prior mental health symptoms and family hardship. Sensitivity analyses suggested that cybervictimization only and both cyber- and face-to-face victimization were associated with a higher risk of suicidal ideation/attempt compared to face-to-face victimization only and no victimization; however, analyses were based on small n. In prospective analyses, cybervictimization was not associated with suicidal ideation/attempt 2 years later after accounting for baseline suicidal ideation/attempt and other confounders. In contrast, face-to-face victimization was associated with suicidal ideation/attempt 2 years later in the fully adjusted model, including cybervictimization. CONCLUSIONS: The cross-sectional association between cybervictimization and suicidal ideation/attempt is independent from face-to-face victimization. The absence of a prospective association suggested short-term effects of cybervictimization on suicidal ideation/attempt.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".