Kindergarten Readiness in Children Who Are Deaf or Hard of Hearing Who Received Early Intervention
Bibliographic record
Abstract
BACKGROUND: Children who are deaf or hard of hearing (D/HH) have improved language outcomes when enrolled in early intervention (EI) before the age of 6 months. Little is understood about the long-term impact of EI on outcomes of kindergarten readiness (K-readiness). The study objective was to evaluate the impact of EI before the age of 6 months (early) versus after 6 months (later) on K-readiness in children who are D/HH. METHODS: In this study, we leveraged data from the Ohio Early Hearing Detection and Intervention Data Linkage Project, which linked records of 1746 infants identified with permanent hearing loss born from 2008 to 2014 across 3 Ohio state agencies; 417 had kindergarten records. The Kindergarten Readiness Assessment was used to identify children as ready for kindergarten; 385 had Kindergarten Readiness Assessment scores available. Multiple logistic regression was used to investigate the relationship between K-readiness and early EI entry while controlling for confounders (eg, hearing loss severity and disability status). RESULTS: = .005). Children who entered early had similar levels of K-readiness as all Ohio students (39.9%). After controlling for confounders, children who entered EI early were more likely to be ready for kindergarten compared with children who entered later (odds ratio: 2.02; 95% confidence interval 1.18-3.45). CONCLUSIONS: These findings support the sustained effects of early EI services on early educational outcomes among children who are D/HH. EI entry before the age of 6 months may establish healthy trajectories of early childhood development, reducing the risk for later academic struggles.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".