Enhancing Understanding of the Nature of Supportive School-based Relationships for Youth who have Experienced Trauma
Bibliographic record
Abstract
Student-teacher relationships play a critical role in supporting the learning and well-being of students with mental health problems. The purpose of this article was to draw from both current literature and previous qualitative interview research to understand the aspects of school-based relationships that are beneficial for students who have experienced trauma. The integration of theory with the first-person accounts of the youth led to the development of a model that describes the core needs created by experiencing trauma and the nature of student-teacher relationships that can meet these needs in the educational context. The four aspects of student teacher relationships that supported trauma-related needs at school were relationships that were 1) teacher driven, 2) authentic caring, 3) attunement to students’ emotional states, and 4) individualized. Establishing caring connections with teachers was pivotal to student health and well-being and to meeting the core needs created by traumatic events (safety, control, trust, self-worth, self-expression, connections). As youth with mental health problems spend considerable time each week in the classroom, a greater understanding of the nature of supportive school-based relationships can inform teachers in their efforts to teach and connect with students. Keywords: mental health, trauma, student-teacher relationships, school connectedness
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".