Trauma-informed Pedagogical Practices in Post-Secondary Education: An Integrative Review of the Literature
Bibliographic record
Abstract
The experience of past or current trauma can interfere with learning in post-secondary education and can be particularly problematic for people from equity-deserving groups. Implementing trauma-informed pedagogical practices could contribute significantly to post-secondary education by cultivating safe and equitable learning spaces that support a range of needs and potentially lead to better academic outcomes. However, the uptake of trauma-informed pedagogy in post-secondary institutions has lagged despite increased attention to students’ experience of trauma. Post-secondary educators may not be aware of trauma-informed pedagogical practices or know how to implement them. Currently there are few comprehensive resources for about trauma-informed pedagogy. The purpose of this paper is to report on the findings of an integrative review of the literature conducted to gather and categorize information regarding trauma-informed pedagogical practices in post-secondary education. The research question guiding this study was: What trauma-informed pedagogical practices can be used in post-secondary education and how can they be applied to promote student success? Integrative review methodology guided the research. Relevant literature was located through a systematic literature review (n=55) and was analyzed in three stages (preparation, coding, creating categories). The Four Proactive Priorities framework guided deductive coding and data analysis. Findings include a comprehensive list of trauma-informed pedagogical practices in four proactive priority areas (connection, empowerment, flexibility, predictability). This first-known comprehensive and systematic review of the literature advances knowledge and understanding of the pedagogical practices that could be implemented to support trauma-informed teaching and learning in post-secondary education.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".