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Record W4392391155 · doi:10.1093/deafed/enad041

Family-Centered Early Intervention Deaf/Hard of Hearing (FCEI-DHH): Call to Action

2024· article· en· W4392391155 on OpenAlexaff
Amy Szarkowski, Bianca C Birdsey, Trudy Smith, Mary Pat Moeller, Elaine Gale, Sheila Moodie, Gwen Carr, Arlene Stredler-Brown, Christine Yoshinaga‐Itano, Daniel Holzinger

Bibliographic record

VenueThe Journal of Deaf Studies and Deaf Education · 2024
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsWestern University
FundersU.S. Department of Health and Human Services
KeywordsIntervention (counseling)Call to actionGlobeAction (physics)PsychologyMedicineMedical educationNeurosciencePsychiatryBusiness

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.173
GPT teacher head0.473
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2024
Admission routes1
Has abstractyes

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