The Childhood Trauma and Attachment Gap in Speech-Language Pathology: Practitioners' Knowledge, Practice, and Needs
Bibliographic record
Abstract
PURPOSE: Previous research demonstrates the relevance of childhood trauma and attachment to communication development. This study aimed to understand speech-language pathology (SLP) practitioners' knowledge, beliefs, training, and current practices regarding developmental trauma and attachment. METHOD: = 97) who work primarily with children from birth to age 6 years in Canada. Quantitative (univariate and bivariate) analysis was performed with SPSS. Qualitative responses were coded by two reviewers using thematic analysis to identify key themes. RESULTS: SLP practitioners are working with children who have experienced trauma and adapt their practice when they are aware of this history. Practitioners also indicated, however, that they lack training with respect to trauma and attachment, their understanding of the concepts is narrow, they do not have standardized practices for obtaining trauma history, and they do not adapt their practice in consistent ways. The results show there is interest in understanding how trauma affects communication development, the relevance to their work, and that additional training is needed to support practitioners to identify and respond to trauma in early childhood. CONCLUSIONS: Findings from this study support SLP practitioners' involvement in early identification of trauma and the development of best practices regarding trauma-informed SLP assessment and intervention. The results also inform how systems and areas of service need to be adjusted to be more accessible, flexible, and collaborative in order to support children and families whose lives have been impacted by trauma and indicate additional areas of research in the area. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.16968097.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".