Embedding equity into school mental health theory, research, and practice: An introduction to the special issue series
Bibliographic record
Abstract
Abstract In this article, we present an introduction and overview for a two‐part special issue series focused on embedding equity into school mental health through theory, research, and practice. The papers in Part 1 of the special issue series cover critical topics for advancing equitable school mental health, including the centering of community and youth perspectives; a focus on reducing disproportionality in discipline practices; and details on equity‐focused interventions. In Part 2, papers focus on integrating race and racial equity into school mental health frameworks; integrating a racial equity lens into trauma‐informed school practices; addressing outcomes that have disproportionate impacts on youth of color; and exploring domains of injustice and oppression beyond racial inequity. We hope that the topics discussed in this special issue series move us towards providing equitable and just school mental health services for all children and youth.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".