Trauma-Informed Pedagogy and Online Social Work Education and Field Instruction
Bibliographic record
Abstract
Trauma-informed care has emerged over the past few decades as critical within school settings, yet, within higher education, trauma-informed pedagogy (TIP) has remained underdeveloped for training and research. Educators in post-secondary institutions require trauma literacy and knowledge of applications within the learning environment, including best practices and strategies for teaching, classroom management, and support of students, to address the high numbers of adults reporting adverse childhood events (ACEs) and exposure to trauma. Specifically, many social work students have higher rates of trauma, making attention to trauma-informed education practices paramount. For many, the unforeseen worldwide pandemic resulted in a hasty transfer from face-to-face to online instruction, and educators were faced with teaching highly sensitive and potentially triggering content in an unfamiliar forum. The effects of this shift for students, their online experiences, and how educators model TIP have yet to be examined. In this chapter, we highlight results from a recent mixed-method study at three Canadian universities to provide a baseline of trauma-informed pedagogy and experiences of social work students. We present a TIP model that reflects the findings of our study and builds on previous trauma-informed approaches that can support students and instructors in higher education environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".