Bringing Trauma to School: Sharing the Educational Experience of Three Youths
Bibliographic record
Abstract
Experiencing a traumatic event can impact students’ well-being and jeopardize their academic achievement and social-emotional health. A quarter of all students will experience a traumatic life event before they graduate from high school (Costello, Erkanli, Fairbank, & Angold, 2002), necessitating an understanding of how trauma affects students in the school context. This paper brings the perspectives of three youths to the forefront and explores their educational experiences and their perception of the role schools play in supporting students who bring trauma to school. A qualitative case study design and personal interviews with the youth led to the findings reported here. The presentation and management of the trauma and resulting stress differed among participants, and overall school experiences ranged from very negative to very positive. Participants were more unified in their perceptions of what they wanted from schools and the role that school could play. Themes across cases emphasized the importance of teacher driven, supportive, caring relationships, and the need for schools to focus on student well-being as well as academic functioning. The protective nature of school connectedness, in increasing engagement and decreasing at-risk behaviours and emotional distress (Blum, 2005; Bond et al., 2007; Klem & Connell, 2004; McNeeley, Nonnemaker, & Blum, 2002), holds promise for students with traumatic stress.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".