Initial teacher education and trauma and violence informed care in the classroom: Preliminary results from an online teacher education course
Bibliographic record
Abstract
Abstract A trauma‐and‐violence‐informed‐care (TVIC) system within an educational setting provides a framework of practice that enables schools to become safe and inclusive places for some of the most vulnerable students. Initial teacher education may provide the opportunity to prepare teachers to create classrooms and learning experiences that are safe, equitable, and meet students' needs. A mandatory mental health literacy course for second year teacher candidates in a Bachelor of Education program (n = 287) at a large Canadian university introduced TVIC concepts. A case study approach was used to illustrate both the challenges that students exposed to trauma and/or violence can experience, as well as strategies and knowledge that teachers can use to support these students. This program evaluation used a repeated measures design to survey both attitudes toward trauma informed care for teachers and self‐efficacy for teaching using inclusive practices before and after the course. A significant effect of time on both measures revealed an increase in both participants' attitudes toward TVIC and their self‐efficacy in using inclusive teaching practices. These findings provide support for the inclusion of these important topics for all teacher candidates. Implications for practice and policy are discussed.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".