Validity of the Communication Function Classification System for use with preschool children with communication disorders
Bibliographic record
Abstract
AIM: To evaluate construct and predictive validity of the Communication Function Classification System (CFCS) for use with preschool children with a range of speech and language disorders. METHOD: Seventy-seven preschool children with speech and language disorders (50 males, 27 females; mean 2y 7mo, standard deviation [SD] 1y) participated in this cohort study. Preschool children had speech and language, language-only, or speech-only disorders. Together with parent input, speech-language pathologists (SLPs) completed the CFCS at time 1. Parents and SLPs then independently completed a validated change-detecting functional communication outcome measure, the Focus on the outcomes of Communication Under Six (FOCUS), three times: at assessment (time 1), at the start of treatment (time 2), and at the end of treatment (time 3). RESULTS: There was a significant negative correlation between CFCS classifications and FOCUS scores at all three measurement points for the ratings by both parents and SLPs (correlations ranged from -0.60 to -0.76). As expected, no correlations between CFCS classifications and FOCUS change scores were statistically significant. INTERPRETATION: This study provides evidence of construct and predictive validity of the CFCS, demonstrating its value as a discriminative tool for use with preschool children with a range of speech and language disorders.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".