Exploring the Views, Perspectives, and Current Practices of Educational Speech-Language Pathologists and Psychologists in Canada: How Childhood Developmental Language Disorders Are Identified and Diagnosed
Bibliographic record
Abstract
PURPOSE: Across Canada, speech-language pathologists (SLPs) and educational psychologists (EPs) work in schools to identify and diagnose childhood learning difficulties, including language disorders; however, both professional groups use different terms to identify and diagnose them. Using the term developmental language disorder (DLD), developed by the CATALISE consortium, would provide consistency across fields. To effectively implement the use of DLD, it is crucial to understand how EPs and SLPs currently identify childhood language disorders and to investigate the potential impact of a practice change in this area. METHOD: The study conducted 13 moderated focus groups and one one-on-one semistructured interview across six Canadian provinces in English and French. RESULTS: We found some social and structural barriers that impact SLPs' and EPs' current practice of identifying and diagnosing language disorders generally (e.g., the belief that children should not be labeled "too early," institutions that prioritize certain professional diagnoses over others, board policies that do not allocate funds for language disorders, professionals' reticence to convey difficult information such as a diagnosis to collaborators) and DLD specifically (e.g., different professional taxonomies, lack of familiarity with or uncertainty about the label, not recognized as a condition in schools that may or may not even identify language disorder as a category of exceptionality). Nevertheless, the focus groups also revealed the extent to which DLD could be useful in their current practice. CONCLUSION: Both EPs and SLPs acknowledged the importance of working together; therefore, DLD could inspire more collaborative practice between SLPs and EPs around language disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".