Applying Trauma- and Violence-Informed Care to Speech-Language Pathology Practice Across the Lifespan
Bibliographic record
Abstract
The high prevalence of trauma world-wide is such that speech-language pathologists are likely to support clients across the lifespan with experiences of trauma, such as abuse, neglect, intergenerational and racial trauma, and exposure to structural and systemic violence. Trauma can affect peoples’ neurobiology and can also impact cognitive, social, and language development and compromise over-all communication competence. Trauma-and-violence informed approaches must be built upon a foundational knowledge of the impact of trauma on people’s lives: from neurobiology and development, to health, communication, and behavior. It is therefore evident that consideration of trauma must be built into training programs, care provision, organizational policies, and programs. To provide trauma- and violence-informed care (TVIC), speech-language pathologists must individually and collectively engage in the process of critical reflection to gain insight into their personal and cultural assumptions and values, and to affect change in practice. To this end, the authors draw from available literature as well as their clinical, academic and individual experiences to illustrate how TVIC can shape speech-language pathologists’ lens with respect to 1. The social determinants of health and access to services, 2. Behaviors that challenge, and 3. Social communication, social cognition, and emotional regulation. The Substance Abuse and Mental Health System Administration’s (SAMHSA) four assumptions and six principles of trauma-informed care are applied to illustrate how TVIC can be incorporated into practice.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.007 | 0.013 |
| 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".