Conceptualizing Trauma-Informed Speech-Language Pathology Practice: Adaptations and Implications Across Levels of Practice
Bibliographic record
Abstract
Purpose: There is an increasing drive to integrate trauma knowledge and the principles of trauma-informed care into speech-language pathology (SLP) practice. While work has been done to move this forward, the concept of trauma-informed SLP practice requires a clearer conceptualization. In this article, we outline our current framework for conceptualizing trauma-informed SLP practice, developed through our work with SLP providers and leaders implementing and researching trauma-informed practice adaptations, and our engagement with mental health experts in childhood trauma and attachment disruption. We explain the key guiding principles that SLP practitioners, leaders, and decision-makers can adopt from best practices in the field of mental health to enhance service delivery, engagement, relationship building, and outcomes for all clients, as well as trauma-informed and trauma-responsive adaptations and implications to all levels of SLP practice. Conclusions: It is important to recognize that many clients may have experienced trauma, whether or not care providers are aware; therefore, our systems should be designed to be responsive to everyone. This clinical focus article underscores the critical need for systemic change, to create supportive environments that foster healing and resilience for all individuals involved in systems and support services.
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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 | 0.052 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.069 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".