Engaging peers to promote well‐being and inclusion of newcomer students: A call for equity‐informed peer interventions
Bibliographic record
Abstract
Abstract Although newcomer youth demonstrate high levels of resiliency, many experience challenges in emotional, linguistic, academic, and social functioning. Over the past decade, some promising school‐based psychosocial interventions for newcomer youth have been developed. These interventions are necessary, but not sufficient to promote well‐being. Without attention to the larger context, focusing solely on the skills and adjustment of newcomer youth could potentially stigmatize students further. There is a need to engage non‐newcomer peers for two reasons. First, peer relationships and inclusion are important predictors of well‐being. Second, from an equity lens, there is a need to create environments that promote youth well‐being; at the very least, these environments must engage non‐newcomer youth in recognizing and combatting discrimination. This study outlines the need for peer‐focused programming to support newcomers and describes existing research on interventions developed to promote peer relationships (e.g., mentoring) or reduce discrimination (e.g., teacher‐led discrimination reduction approaches). We identify other intervention models that could inform how to add an equity lens to school mental health intervention, including how a gender‐sexuality alliance model could be adapted, and how equity considerations could be integrated into bystander approaches. We conclude with specific implications and recommendations for embedding equity into school mental health.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".