Balancing the Equation: Trauma-Informed Practices for Equity in Student Academic Achievement and Well-Being Outcomes
Bibliographic record
Abstract
Adverse childhood experiences and trauma can have detrimental effects on students' academic performance and overall well-being, often resulting in inequitable academic and social outcomes. The problem of practice addresses how to build capacity for trauma awareness and trauma-informed practices in educators to support students impacted by trauma and their classmates. The organization involved in this change process is a large, suburban elementary school in Ontario, serving students in kindergarten through to grade eight. The Dissertation-in-Practice provides a framework for educators to better support students impacted by adverse experiences and/or trauma, through the development of socio-emotional learning competencies and the provision of psychological safety within their classrooms. Transformative and trauma-informed leadership approaches will be embedded within a layered framework that combines the change path model and the Missouri model for trauma-informed schools and applied to the problem of practice. Possible solutions are presented and include job-embedded professional learning, a classroom-based socio-emotional learning program, and parent engagement to mitigate the impacts of adverse experiences and trauma and achieve an envisioned future state. The preferred solution engages educators and students through a classroom-based socio-emotional learning program and offers change agents the opportunity for collaborative capacity building, while providing immediate supports for students. Enhancing psychological safety within each classroom will provide opportunities for school-wide transformation that will mitigate the impacts of trauma and support equitable academic and well-being outcomes for all students.\n Keywords: Trauma, adverse childhood experiences, equity, socio-emotional learning, transformative leadership, trauma-informed leadership
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.009 |
| 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".