A narrative study of trauma-informed programs in an early elementary education setting
Bibliographic record
Abstract
Trauma has a life-long negative impact on the growth and development of many children. School-based trauma-informed interventions provide the opportunity to offer cost effective mental health support to children who may not otherwise access counselling. Previous research has found that many school counsellors and teachers report feeling inadequately prepared to support traumatized children. Additionally, many teachers and school counsellors report never receiving training in trauma-informed practices. There is a current lack of empirical Canadian research on the use of trauma-informed practices by school counsellors, particularly with young children. The present study investigated the stories of school counsellors and teachers using trauma-informed practices when supporting young children (ages five to eight) through narrative inquiry. Narrative interviews were conducted with two school counsellors and two teachers. Narrative thematic analysis was employed to construct themes and the verification process included member checking procedures. Four themes emerged from the analysis of participants’ narratives: Being Introduced to Trauma-Informed Practices, Trauma-Informed Practices as a Whole School Approach, Barriers to Trauma-Informed Approaches, and Strengths and Success of Implementing Trauma-Informed Approaches. The constructed themes are presented and the implications of the findings, and recommendations for future educational policy, practice, and research are discussed.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".