Teaching English Language Learners Who Have Trauma Experiences: Healthy Boundaries, Happy Teachers
Bibliographic record
Abstract
English language teachers, especially those working with refugees and vulnerable populations, are at risk of empathy-based stress (e.g., burnout, compassion fatigue, vicarious trauma). Due to conditions prevalent in the English language teaching context and relationships of trust that develop classrooms, instructors may inadvertently be exposed to and impacted by learner trauma. Over time, empathetic engagement and hearing troubling stories can result in vicarious trauma. One key factor that puts instructors at risk of vicarious trauma and other empathy-based stress is unclear boundaries. In this article, we draw on data from 44 semi-structured ethnographic interviews with language instructors who self-identified as being negatively impacted by their work with learners who have had trauma experiences. We report specifically on themes related to boundaries that emerged from the data. The findings focus on factors that contribute to crossed boundaries, such as overfamiliarity, role misperception, a saviour mentality, and dual relationships. We also describe benefits of and strategies for setting and maintaining boundaries. The article concludes with implications and recommendations for policy makers, organizational decision makers, and English language instructors.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".