Impact of Trauma-Informed Training and Mindfulness-Based Social–Emotional Learning Program on Teacher Attitudes and Burnout: A Mixed-Methods Study
Bibliographic record
Abstract
Abstract A trauma-informed approach can give teachers the strategies they need to help children affected by trauma reach their full potential in the classroom. Mindfulness-based social–emotional learning (SEL) programs equip teachers with essential tools to create a trauma-informed classroom, which in turn helps alleviate stress associated with supporting trauma-impacted children. Because existing research on SEL programs has predominantly focused on student well-being, there is a paucity of research examining teacher outcomes and the integration of a trauma-informed framework. The purpose of the study was to investigate the benefits of trauma-informed training and MindUP delivery on educator attitudes and burnout. Intervention educators received trauma-informed and MindUP training and implemented MindUP in their classrooms. Comparison educators did not participate in training and taught their usual curriculum. We compared trauma-informed attitudes and burnout levels among 112 educators ( n = 71 intervention, n = 41 comparison) using the Attitudes Related to Trauma-Informed Care (ARTIC) scale and the Maslach Burnout Inventory. Pre- and post-intervention quantitative data were augmented by qualitative focus group data. Results showed that educators in the intervention group reported significant decreases in emotional exhaustion, and significant improvements in the reactions subscale and overall scores on the ARTIC scale. Greatest improvements in self-efficacy and personal accomplishment were observed among educators who implemented MindUP for two consecutive years. These findings were supported by focus group data. Our results show that infusing trauma-informed training with an existing mindfulness-based SEL intervention may encourage teachers to embrace trauma-sensitive attitudes and reduce burnout.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".