Emploi du terme « Trouble développemental du langage » par les orthophonistes œuvrant en contexte linguistique minoritaire francophone.
Bibliographic record
Abstract
Context: Specific and universal terms for identifying children with persistent language impairment vary widely among speech-language pathologists nationally and internationally. Between 2017 and 2021, a multidisciplinary international consensus on the terminology and approach to the diagnosis of developmental language disorder (DLD) was reached (Bishop et al., 2017; Maillart et al., 2021; Robillard, 2019). In French-speaking minority communities in Canada, the identification of DLD is further complicated because the coexistence of two (or more) languages often requires a lengthy and comprehensive assessment to confirm the presence of a disorder. Objectives: The objectives of this study were to determine whether speech-language pathologists working with children attending French minority schools use the term DLD (implying the act of making a diagnosis) and how speech-language pathologists assess and identify DLD. Methods: Eighty-six speech-language pathologists working in a francophone minority community in Canada completed a survey regarding the diagnosis and use of the term “developmental language disorder” with children attending French-language schools, and to identify barriers to the use of this term. Results: In summary, the results indicated that 73.3% of speech-language pathologists surveyed use the term DLD. Participants reported a significant need for training and awareness (amongst teachers and families) regarding the diagnosis and use of the term. In addition, a significant lack of material and human resources was raised. Five recommendations to increase the frequency of use of the Developmental Language Disorder term and to improve language assessment in minority language settings were made. Conclusion: In order to increase awareness of DLD and its effects on a child’s daily life, academic performance, and social life, training for families, teachers, and speech-language pathologists is needed. In addition, it is important to address the lack of scientific research as well as the shortage of human and material resources in the minority language context in order to better assess, diagnose and intervene with children who have language difficulties.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".