Why Screening Canadian Preschoolers for Language Delays Is More Difficult Than It Should Be
Bibliographic record
Abstract
We examined the ability of four American screening tests to identify preschool-age Canadian children with language delays. At 54 months, 110 children from five Ontario infant and child development programs completed the Ages and Stages Questionnaire, Battelle Developmental Inventory Screening Test, Brigance Preschool Screen, and Early Screening Profiles. Their results on the language measures were then compared with their performance on the Preschool Language Scales, 4th ed., and the Bracken Basic Concepts Scale—Revised at 5 years. None of the screening tests had adequate sensitivity (SN) and specificity (SP) when identifying receptive language delays; only one screen had adequate SN and SP for expressive language delays. Adjusting cutoffs based on ROC curve analyses improved the ability of some screens to identify language delays, but combining tests did not improve discriminability. Our results indicate that language screening measures are not interchangeable. We recommend the provision of detailed SN and SP information for each scale of screening tests so that early interventionists can evaluate the adequacy of each component of a screening test. When norming tests, appropriate analyses should be conducted to determine whether American norms are appropriate for use with Canadian children, given the differences in the demographics and educational systems of the two countries.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".