Graduate Student Clinicians' Perceptions of Child Speech Sound Errors
Bibliographic record
Abstract
Purpose: Speech-language pathologists (SLPs) rely on auditory perception to form judgments on child speech. This can be challenging for graduate student clinicians with limited clinical experience as they often need to judge children's speech errors using their auditory perception. This study examined how consistently graduate student clinicians used a 3-point perceptual rating scale to judge child speech. Method: Twenty-four graduate student clinicians rated single words produced by children with typically developing speech and language skills and children with speech sound disorders. All participants rated the productions using a 3-point scale, where "2" was an accurate production, "1" was a close approximation, and "0" was an inaccurate production. Ratings were solely based on the auditory signal. These ratings were compared to a consensus rating formed by two experienced SLPs. Results: Graduate student clinicians reached substantial agreement with the expert SLP rating. They reached the highest percentage agreement when rating accurate productions, and the lowest agreement when rating inaccurate productions. Conclusions: Graduate student clinicians reached substantial agreement with expert SLP rating in judging child speech using a 3-point scale when provided with detailed descriptions of each rating category. These results are consistent with previous findings on the role that clinical experience plays in speech error perception tasks and highlight the need for additional listening training in speech-language pathology graduate programs.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".