A Qualitative Study Exploring Teachers’ Understanding of Trauma and Trauma-Informed Education in Saskatchewan
Bibliographic record
Abstract
The prevalence of trauma in children and its impact on learning and behaviours is well documented in the literature (Felitti et al., 1998, or Desautels, 2020). Without sufficient education on how to support students who have been impacted by trauma, even the most skilled and compassionate teacher can unintentionally exacerbate students’ learning experiences and cause further harm (Robson & Mastrangelo, 2018) . Although trauma-informed education programs with enhanced teacher training are emerging, many teachers are working in settings that have not yet adopted this approach. This study aimed to address the gaps regarding the understanding of trauma-informed capacity of Saskatchewan teachers who are working in schools without established trauma-informed programs. The two research questions that guided the study included: (a) What are Saskatchewan elementary school teachers’ understanding of trauma?, (b) How may/have teachers benefit(ed) from trauma-informed education?. Semi-structured interviews were used to collect data from six elementary teachers from two school divisions in the province. The data was analyzed using the six phases of thematic analysis identified by Braun & Clarke (2006). The results yielded four themes: (a) a lack of trauma-informed education, (b) the large extent of trauma, (c) the teacher’s role, and (d) the value of relationships. Recommendations include the need for trauma education for in-service and pre-service teachers which may impact the educational experience of students.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".