The National Prevalence of Adolescent Dating Violence in Canada
Bibliographic record
Abstract
PURPOSE: The national prevalence of adolescent dating violence (ADV) in Canada is currently unknown. This study presents the first nationally representative Canadian data on prevalence and correlates of ADV victimization and perpetration. METHODS: This study analyzed data from the 2017/2018 Health-Behavior in School-Aged Children (HBSC) dataset. Youth from all 10 provinces and two territories participated. The analysis sample includes 3,711 participants (mean age = 15.35) in grades 9 and 10 who reported dating experience in the past 12 months. Youth were asked to report on physical, psychological and cyber ADV victimization and perpetration. To explore correlates of ADV, we included grade in school; gender (male, female or non-binary); race/ethnicity; family structure; immigration status; family affluence; food insecurity; and body mass index. RESULTS: We found that over one in three Canadian youth who had dated experienced and/or used ADV in the past 12 months. Specifically, past 12-month ADV victimization prevalence was 11.8% (95% CI: 10.4, 13.0) for physical aggression; 27.8% (25.8, 30.0) for psychological aggression; and 17.5% (15.8, 19.0) for cyber aggression, while perpetration prevalence was 7.3% (6.2, 9.0) for physical aggression; 9.3% (8.0, 11.0) for psychological aggression; and 7.8% (6.7, 9.0) for cyber aggression. Both victimization and perpetration were highest among non-binary youth (as compared to cisgender males and females). Overall, use and experience of ADV was greatest among youth experiencing social marginalization (e.g., poverty). CONCLUSIONS: ADV impacts a substantial minority of Canadian youth, and is a serious health problem. ADV prevention programs that focus on root causes of violence (e.g., poverty) are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".