Toward a Trauma‐Informed Comprehensive School Physical Activity Program: Specifying Feasible Practices Across the School Day
Bibliographic record
Abstract
BACKGROUND: Many students would benefit from trauma-informed physical activity (PA); however, there is a lack of systematic guidance on incorporating trauma-informed practices across school-based PA opportunities. The purpose of this study was to generate a feasible framework for trauma-informed school-based PA. METHODS: Framework development was guided by a modified Delphi approach, including an exploration phase and an evaluation phase. First, a multidisciplinary working group reviewed extant literature, mapping trauma-informed practices onto the domains and components of the Comprehensive School Physical Activity Program (CSPAP). Subsequently, experts (n = 14) provided quantitative feedback on the utility and feasibility of these practices. RESULTS: Thirty-three practices met pre-determined thresholds for utility and feasibility. Across CSPAP domains, this included attending to: (a) safety; (b) positive communication and relationships; (c) empowerment, choice, and voice; (d) emotion management and skill building; (e) instructor competencies and support; and (f) cultural, historical, and gender issues. IMPLICATIONS FOR SCHOOL HEALTH POLICY, PRACTICE, AND EQUITY: Incorporating trauma-informed practices into existing frameworks and approaches to school-based PA may promote uptake of practices that help meet the social and emotional needs of trauma-exposed youth. CONCLUSIONS: The present output provides a foundation for generating tools and supports for incorporating trauma-informed practices into school-based PA promotion efforts.
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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 | 0.050 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".