Trauma-informed practices and physical education: A scoping literature review
Bibliographic record
Abstract
Children and youth around the world are experiencing trauma to the extent that it has been labeled a global health concern ( O’Toole, 2022 ). Physical education (PE) teachers, among others, are attempting to respond to this concern by implementing research-based trauma-informed practices (TIPs). The purpose of this paper was to conduct a scoping review to gather and synthesize the literature on TIPs appropriate for the PE context within the K-12 school system in the Global North. Following the five-stage scoping review protocol outlined by Peters et al. (2020) , the process returned 34 articles that met all inclusion criteria. The majority of the literature (18 articles) was found in practitioner-focused journals and centered on providing knowledge and broad advice for PE teachers. Most of the remaining work included qualitative research and focused on exploring the experiences of PE teachers working with students who had experienced trauma. Only four studies included an intervention. In addition to discussing the available research, this article explores the various conceptions of trauma that have recently appeared in the research as well as the implications of trauma's and TIPs’ infusion into school culture. The review clearly demonstrates a need for continued discussion around trauma, more research in the area of TIPs (specifically related to PE), and continued professional development for teachers.
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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 | 0.019 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".