Frequency of Early Intervention Sessions and Vocabulary Skills in Children with Hearing Loss
Bibliographic record
Abstract
BACKGROUND: A primary goal of early intervention is to assist children in achieving age-appropriate language skills. The amount of intervention a child receives is ideally based on his or her individual needs, yet it is unclear if language ability impacts amount of intervention and/or if an increased frequency of intervention sessions results in better outcomes. The purpose of this study was to determine the relationship between the frequency of early intervention sessions and vocabulary outcomes in young children with hearing loss. METHODS: This was a longitudinal study of 210 children 9 to 36 months of age with bilateral hearing loss living in 12 different states. Expressive vocabulary skills were evaluated using the MacArthur-Bates Communicative Development Inventories. RESULTS: A higher number of intervention sessions reported at the first assessment predicted better vocabulary scores at the second assessment, and more sessions reported at the second assessment predicted better scores at the third assessment. For each increase in the number of sessions reported, there was a corresponding, positive increase in vocabulary quotient. In contrast, children's vocabulary ability at an earlier time point did not predict intervention session frequency at a later point in time. CONCLUSIONS: A significant prospective effect was apparent with more therapy sessions resulting in improved vocabulary scores 9 months later. These findings underscore the importance of early intervention. Pediatricians and other health care professionals can help apply these findings by counseling parents regarding the value of frequent and consistent participation in early intervention.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".