Mapping out a Canadian university’s trauma-informed landscape: A preliminary exploration
Bibliographic record
Abstract
Adversity and trauma are commonly misunderstood human experiences affecting most individuals across post-secondary campuses. Depending on contextual factors, they can lead to lifelong challenges or growth. Without an adequate understanding, well-meaning individuals and organisations may unknowingly perpetuate harm. Trauma-informed approaches (TIAs) can help organisations prevent harm, promote empowerment, and enhance connection. Preliminary investigations are critical for the development of sustainable TIAs. This exploratory study investigated the attitudes, knowledge, and workplace culture concerning adversity and trauma at a small Canadian university with no existing TIA. The study involved two phases: first, preliminary consultations with ten community stakeholders and a review of relevant literature, and second, nine semi-structured interviews with staff and faculty, followed by reflexive thematic analysis of the data. Four key themes emerged: (1) limited trauma awareness on campus, highlighting a need for system-wide training and tools, (2) privilege and vulnerability, disparities in safety, flexibility and agency across university departments, (3) pockets of safety, academic culture and other barriers to change, (4) suggestions to address barriers, through trauma education, human-centric practices, and systemic collaboration. This study provides insight into the readiness, challenges, and recommendations for developing system-wide trauma awareness in a Canadian university.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.033 | 0.008 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".