Mitigating The Impacts of Secondary Trauma in K-12 Educators
Bibliographic record
Abstract
Given the prevalence of adversity experienced by students attending K-12 schools, educator roles have evolved to include supporting student mental health through trauma-informed practice. What remains largely unaddressed, are the impacts of student trauma on the educators who support them, as well as best practices for alleviating this stress in school personnel, as part of a trauma-informed approach. With secondary trauma being an understudied construct in educators, this systematic literature review provides an overview of studies on how secondary trauma impacts educators, as well as approaches to supporting educator mental health following indirect trauma exposure. Qualitative data analysis resulted in the identification of themes surrounding secondary trauma impacts, including emotional and psychological affects, burnout and compassion fatigue, educator attrition, and effects on student outcomes. Themes related to mitigation strategies included individual and organizational approaches. Although best practices for mitigating secondary trauma in educators include a combination of interventions, organizational practices superseded those required by individuals alone. Thus, this review also calls for greater attention from administrators and educational policymakers to invest in further research and management of secondary trauma at an organizational level. A limitation of this research is the lack of empirical information on the impacts of secondary trauma in educators, and research-based ways to support staff affected, given the dearth of studies that currently exist. Keywords: secondary trauma, educators, school personnel, mental health, organizational practices
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".