Reliability of Speech-Language Pathologists' Categorizations of Preschoolers' Communication Impairments in Practice
Bibliographic record
Abstract
Purpose An efficient and reliable way to categorize children's communication impairments based on routine clinical assessments is needed to inform research and clinical decisions. This preliminary study assessed interrater reliability of speech-language pathologists' categorization of preschoolers' speech, language, and communication impairments using a clinical consensus document. Method Six speech-language pathologists at three community sites worked in pairs to assess 38 children aged 1-5 years, then used the clinical consensus document to categorize children's communication impairments broadly. Identified language and speech sound impairments were further subcategorized. Results Speech-language pathologists had substantial to almost perfect agreement for three broadly focused impairment categories. Agreement for whether language difficulties/disorders were developmental or associated with a biomedical condition was almost perfect, but moderate for whether difficulties impacted receptive or expressive language, or social communication skills. Agreement was fair for rule-based speech delays/disorders, but low for motor-based and mixed speech impairments. Conclusions Results support use of the clinical consensus document to collect data for reliable categories. Additional work is needed to confirm reliability for some broadly focused impairment categories and for subcategorization of speech impairments.
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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 | 0.066 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".