Understanding dating violence mental health outcomes in adolescents : age discrepancy and suicidality
Bibliographic record
Abstract
Purpose: Key mental health outcomes are associated with adolescent dating violence. In research examining adolescent dating violence and mental health outcomes, results vary between studies, and little research is from Canada. Current research seldom considers gender differences, and the link between dating violence and older romantic partners. This study investigated gender differences in mental health issues associated with past year dating violence among youth in British Columbia. I also analyzed the relationship between dating violence and age-discordant relationships (illegal older partner at first sex). Methods: I conducted an analysis using cross-sectional data from the 2013 BC Adolescent Health Survey (BC AHS; N=28,992). Measures included: past year suicidal ideation/attempts and self-harm behavior, past month extreme sadness and hopelessness, poor self-rated mental health, and self-reported post-traumatic stress disorder (PTSD) and depression diagnoses. I examined the relationship between dating violence and these mental health outcomes separately by gender. I analyzed these same measures by age-discordant relationships among those with a dating violence history for both boys and girls together, and then separately by gender. Results: Among the 39% of youth who reported being in a relationship, 5.9% reported experiencing dating violence, with similar prevalence for boys (6.3%) and girls (5.5%). Adolescent dating violence was associated with all mental health outcomes. Girls reported more adverse mental health outcomes than boys. Youth with dating violence were also more likely to report an age-discordant first relationship, and they were more likely to report poor/fair mental health, PTSD, and suicidality versus those whose first sexual partner was not illegally older. Among those with both dating violence and age discordant relationship history, boys were more likely to report poor/fair mental health; girls were more likely to report PTSD. Discussion and Implications: More girls than boys showed unfavourable mental health outcomes associated with dating violence. Results support past research showing a strong association between an older first sexual partner, dating violence and adverse mental health outcomes. Nurses in mental health services should regularly screen for dating violence in adolescent romantic relationships, for both boys and girls, which may exacerbate mental health illness.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".