Understanding the why: The integration of trauma-informed care into speech and language therapy practice
Bibliographic record
Abstract
This article aims to highlight the need to integrate Trauma-Informed Care (TIC) into the practice of Speech and Language Therapy. TIC is a strength-based framework underpinned by an understanding and responsiveness to the widespread pervasiveness and impact of trauma. The literature on TIC within the field of Speech and Language Therapy is in its infancy but is progressing. In this context, there is an absence of clear guidelines for TIC in the field to support providers and administers to understand the relevance, underlying theory, and application to practice. In this paper we outline the theoretical underpinnings and application to practice. We argue that the profession requires an ongoing commitment to continuous research to corroborate communication-specific best practices of TIC to support clinicians in translating those findings into practice to best support clients.
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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 | 0.049 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.051 |
| Scholarly communication | 0.027 | 0.029 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".