The Association Between Teen Dating Violence and Concussion
Bibliographic record
Abstract
PURPOSE: In adults, intimate partner violence victimization and traumatic brain injuries, including concussion, are strongly connected. However, no prior research has explored this association among youth. This study explores the association between teen dating violence (TDV) and concussion to inform clinical care for these at-risk groups. METHODS: We used data from ninth and 10th grade youth in the 2017/18 Health Behavior in School-Aged Children nationally representative Canadian dataset (N = 2,926). TDV in the past 12 months was measured using three items for victimization and three for perpetration. Youth were asked if they had been told by a doctor or nurse that they had had a concussion in the past 12 months and where they were and what they were doing when they had the concussion. We used coarsened exact matching to create equivalent groups of TDV victims and nonvictims, and then explored the association between TDV and concussion using doubly robust logistic regression models. We also explored effect modification by gender. RESULTS: TDV was related to higher odds of concussion, both overall and when restricted to nonsport settings. In nonsport settings, youth who reported mutual TDV reported the highest odds of past-year concussion (adjusted odds ratio = 2.14, 95% confidence interval: 1.07, 4.28, p = .032). We also found that girls and nonbinary youth reported elevated risk of concussion in the context of TDV. DISCUSSION: We found that TDV was associated with increased risk for concussion. Findings can be used to inform future research and may assist adolescent health providers who treat youth with concussion.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".