Friendship quality and ethnocultural boys: An exploratory evaluation of the WiseGuyz Program
Bibliographic record
Abstract
Abstract Tier 1 school mental health programs for middle school youth often focus on healthy relationships promotion. However, the vast majority of these programs take a gender‐ and race‐neutral approach (i.e., content that does not focus on the way that gender, race, and ethnicity intersect to shape relationships and mental health). Embedding these intersections into Tier 1 programs is critical to equitably advancing mental health for middle school youth. This article specifically explores associations between participation in a Tier 1 gender‐transformative healthy relationships program and friendship quality for Ethnocultural boys. Data were drawn from 278 White and Ethnocultural boys who participated in the program in 2016–2017 or 2017–2018 in a Western Canadian province. Data were analyzed using three‐level multilevel models. In these data, we found that Ethnocultural boys who participated in WiseGuyz reported improved friendship quality with their closest same‐sex friend following the end of the program. We also found that Ethnocultural boys who reported a positive change in male role norms related to emotional restriction reported significant improvements to friendship quality from pre‐ to post‐test. Findings suggest the importance of embedding equity into Tier 1 school mental health programming through a specific focus on intersections between gender, race, and ethnicity.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".