Addressing Trauma and Mental Health in the Inclusive Classroom: An SEL Program Based on DBT Skills and Mental Health Literacy
Bibliographic record
Abstract
The inclusion of students with mental health challenges requires trauma informed care in order for students to successfully manage the stress of the academic environment and the expectations of social interactions (Blitz, Anderson, Saastamoinen, 2016). There are many students with mental health challenges in public schools in Canada not getting the support they need, as only 20% of students requiring mental health services are currently receiving support (Statistics Canada, 2016). Two school districts in two large Canadian cities participated in a pilot study to investigate the outcomes of a universally designed mental health program based on Mental Health Literacy (Kutcher, Wei, & Coniglio, 2016) and Dialectical Behavior Therapy (DBT) skills modules (Miller, Rathus, & Linehan, 2006). Data was collected three times in the year from 40 teachers and 995 students in grades 3-12 related to implementation and fidelity, and student self-reported sense of belonging, resiliency, self-concept and classroom climate. Data included surveys, artifacts, and interviews. Analyses using R for HLM indicated large effect sizes for all four variables, and themes related to resiliency, and the importance of school based social support networks. Results will be discussed in terms of implications for implementation and future research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".