Trauma-Informed Pedagogy: The Prevalence of Trauma Among Students in a Master of Science Program in Occupational Therapy
Bibliographic record
Abstract
Trauma impacts learning at all stages of education and can be particularly problematic at post-secondary levels and for people from equity-deserving groups. Understanding trauma can support effective teaching and learning. Research suggests that students who have experienced trauma may be more likely to enter healthcare professional programs. Research specific to occupational therapy (OT) students who have experienced trauma is limited. The purpose of this study was to explore the prevalence of trauma among Master of Science (MSc) OT students at a Canadian university. The Childhood Trauma Questionnaire (CTQ) was selected for data collection. CTQ is a validated retrospective, self-report tool evaluating five sub-scales of trauma. Respondents (N = 85) were year #1 or year #2 students in an MSc OT program. Descriptive statistics were used to analyze data. CTQ assigns minimization/denial scores which identify possible under-reporting of trauma. Varying severity of trauma was identified, with the highest level of trauma reported on the emotional abuse sub-scale (low to moderate classification). Mean trauma score for the remaining four sub-scales fell within the none to minimal trauma classification. Results suggest a low level of trauma among MSc OT students. However, minimization/denial scores suggest possible under-reporting for 28% of respondents. Trauma can interfere with learning and can manifest in a variety of ways. Considering the potential under-reporting of childhood trauma experiences in retrospective measures, implementing trauma-informed pedagogical practices universally could address the needs of identified trauma survivors while supporting all students including those who do not disclose or underreport.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".