Validity of the Early Years Check-In (EYCI) in a Cross-Sectional Sample of Families
Bibliographic record
Abstract
Background: The objective of the present study was to develop and test the validity of the Early Years Check-In (EYCI), a new tool that measures parent and educator concerns regarding children’s development. The study examined the EYCI’s agreement with 3rd edition of the Bayley Scales of Infant and Toddler Development (BSID-III) an established measure of child development. Two possible thresholds were explored: one to identify children with a probable delay, and another to identify children at the borderline functioning threshold. Methods: Parents of children aged 18 to 42 months were recruited from childcare settings across Ontario, Canada. The study proceeded in two phases. Phase I, intended to pilot the measure, included 49 children. Phase II, a test of the validity of the final version, included 199 children. Parents and educators completed the EYCI for the child, while a blinded assessor completed the BSID-III. Results: The EYCI demonstrated good sensitivity and specificity (86% and 82%, respectively) as a parent-completed tool that identifies children with a probable delay. However, the positive predictive value (15%) suggests the EYCI is likely to over identify children. When identifying children who demonstrated borderline delay, the EYCI demonstrated good sensitivity (80%) but poor specificity (49%). Results from educator-completed EYCIs were poor for both probable and borderline delay. Conclusions: While further research is required, the EYCI shows promise as a parent-completed tool, particularly to identify more-severe cases of delay. Results with educators were poor overall. Future research investigating accuracy of educators in different types of early childcare centres is needed.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".