Exploring Canadian educators’ understandings of trauma-informed education
Bibliographic record
Abstract
The high prevalence of childhood trauma and its association with negative outcomes has been well-documented within the literature. Trauma-Informed Education (TIE) is a teaching approach where educators learn to understand and recognize trauma, create safe spaces, and foster a learning environment which supports children and youth affected by trauma. Much of the research about TIE indicates that educators’ trauma-informed knowledge and attitudes play a large role in whether teachers adopt a trauma-informed approach. However, the majority of research about TIE has taken place outside of Canada, meaning there is little known about Canadian teachers’ attitudes and knowledge about TIE, as well as the trauma-informed training currently utilized in Canada. Therefore, the current study was conducted to gain a better understanding of these factors. Using responses from 173 teachers across Canada, this study found that 63.60% of the participating educators have experienced formal trauma-informed training, indicating that TIE training is available to educators in Canada, and that many educators are interested in, and receiving, this training. Using Spearman’s rank correlations, this study found that educators with greater trauma-informed training experience show higher levels of trauma-informed knowledge, and those with greater years of teaching experience show more positive trauma-informed attitudes. Further, those who are learning about trauma-informed approaches, whether formally or informally, show more positive trauma-informed attitudes, and greater trauma-informed knowledge. The outcomes of this research contribute to the growing information about TIE in Canada and identify ways to increase the use of TIE in Canadian schools.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".