Journal of Early Hearing Detection and Intervention: Volume 10 Issue 2, pages 1-80
Bibliographic record
Abstract
The purpose of this study was to investigate the perceptions and experiences of early childhood intervention (ECI) professionals concerning timely intervention for children who are deaf or hard of hearing (DHH) in Texas.The data for this study was collected through semi-structured, qualitative interviews of 10 ECI practitioners in Texas.Interview data analysis followed the six-phase framework of Thematic Analysis.The data collected identified three major themes: (a) roles and responsibilities, (b) family attributes and experiences, and (c) "deaf" is different.Overall, the findings reveal that the referral and service process for infants and toddlers who are DHH in Texas Early Childhood programs is highly variable, shaped by diverse professional roles, family circumstances, and systemic barriers.ECI professionals often wear multiple hats and navigate complex relationships with numerous school districts, medical providers, and Teachers of the Deaf (TOD).Families' geographic location, access to resources, and readiness to accept the diagnosis significantly influence service enrollment.Unique challenges such as delays in medical paperwork, limited pediatric audiologists, the TEHDI system, and TOD involvement underscore that "deaf is different" from other disability categories.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".