The Effect of Sign Language on the Language Development of Deaf and Hard-of-Hearing Children: A Systematic Review
Bibliographic record
Abstract
Introduction: To systematically review and evaluate the evidence regarding the effect of sign language on language development in deaf and hard-of-hearing children. Materials and Methods: A comprehensive search of electronic databases, including PubMed/ MEDLINE, Web of Science, Scopus, EMBASE, Google Scholar, and ProQuest, from 1995 to April 2024, with no language restrictions, was conducted. Two authors independently assessed the risk of bias using the Newcastle-Ottawa scale (NOS). Results: Six studies involving 259 participants found that exposure to sign language benefits language development in deaf children using hearing aids or cochlear implants (CIs). Children exposed to sign language showed similar or even better spoken language skills than those with limited exposure to sign language. Encouraging parents to learn sign language can significantly support deaf children’s communication and language development. Conclusion: Deaf children with CIs benefit most from communication approaches tailored to their needs. Early intervention, parental involvement, and a rich language environment (signed or spoken) are crucial. While sign language exposure shows promise, further research is needed, especially on its long-term effects and use by hearing parents.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".