Preventing adolescent dating violence: An outcomes protocol for evaluating a gender-transformative healthy relationships promotion program
Bibliographic record
Abstract
Adolescent dating violence (ADV) is a pressing public health problem in North America. Strategies to prevent perpetration are needed, and a substantial body of research demonstrates the importance of applying a gender lens to target root causes of adolescent dating violence as part of effective prevention. To date, however, there has been limited research on how to specifically engage boys in adolescent dating violence prevention. In this short communication, we describe the protocol for a longitudinal, quasi-experimental outcome evaluation of a program called WiseGuyz. WiseGuyz is a community-facilitated, gender-transformative healthy relationships program for mid-adolescent male-identified youth that aims to reduce male-perpetrated dating violence and improve mental and sexual health, by allowing participants to critically examine and deconstruct male gender role expectations. The primary goal of this evaluation is to explore the impact of WiseGuyz on adolescent dating violence outcomes at one-year follow-up among participants, as compared to a risk- and demographically-matched comparison group. Knowledge generated and shared from this project will provide evidence on if and for whom WiseGuyz works, with important implications for adolescent health and well-being.
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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 | 0.036 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.048 | 0.009 |
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".