Family-Centered Early Intervention Deaf/Hard of Hearing (FCEI-DHH): Call to Action
Bibliographic record
Abstract
This Call to Action is the eighth and final article in this special issue on Family-Centered Early Intervention (FCEI) for children who are deaf or hard of hearing (DHH) and their families, or FCEI-DHH. Collectively, these articles highlight evidence-informed actions to enhance family well-being and to optimize developmental outcomes among children who are DHH. This Call to Action outlines actionable steps to advance FCEI-DHH supports provided to children who are DHH and their families. It also urges specific actions to strengthen FCEI-DHH programs/services and systems across the globe, whether newly emerging or long-established. Internationally, supports for children who are DHH are often siloed, provided within various independent sectors such as health/medicine, education, early childhood, and social and disability services. With this Call to Action, we urge invested parties from across relevant sectors to join together to implement and improve FCEI-DHH programs/services and systems, build the capacity of early intervention (EI) Providers and other professionals, extend research regarding FCEI-DHH, and fund EI supports, systems, and research, all with the aim of advancing outcomes for families and their children who are DHH.
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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 | 0.065 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.033 | 0.046 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".