Teen dating violence and teens’ self-efficacy for helping and help-seeking behaviour
Bibliographic record
Abstract
Teen dating violence (DV) is an important public health issue. It is highly prevalent and can have serious consequences, such as internalizing and externalizing problems and higher risk for revictimization. Prevention initiatives aim to encourage help-seeking behaviour among potential victims and offenders. Due to the great importance of and reliance on peers in adolescence, DV prevention initiatives should also involve a focus on peers as potential informal helpers and aim to raise awareness among peer bystanders. A few of such prevention strategies are being implemented and center on the willingness and ability of peers to safely and effectively act prior, during or after DV incidents against others. In this paper, we explore the perceived ability of adolescents to deal with dating violence against themselves or others and to verify whether self-efficacy to deal with DV is associated with gender and self-reported history of DV. We draw upon quantitative data collected among 259 14-18 year olds in Quebec, Canada. Results reveal a significant difference in scores for boys and girls for helping behaviour and help-seeking behaviour. Girls feel most confident to encourage a victim of DV to talk to someone they trust, while boys feel most confident they could directly intervene when they witness someone being hit. Generally, participants seem more confident to help someone else than to seek help for oneself. The findings suggest that dating violence prevention can build on teens’ self-efficacy to deal with dating violence and offer the necessary cues for the design and implementation of effective prevention initiatives.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".