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Record W4392391167 · doi:10.1093/deafed/enad040

Family-Centered Early Intervention Deaf/Hard of Hearing (FCEI-DHH): Structure Principles

2024· article· en· W4392391167 on OpenAlexaff
Amy Szarkowski, Elaine Gale, Mary Pat Moeller, Trudy Smith, Bianca C Birdsey, Sheila Moodie, Gwen Carr, Arlene Stredler-Brown, Christine Yoshinaga‐Itano, Michele Berke, Doris Binder, Natasha Cloete, Jodee Crace, Kathryn Crowe, Frank Dauer, Janet DesGeorges, Evelien Dirks, Johannes Fellinger, Bridget Ferguson, Anita Grover, Johannes Hofer, Sonja Myhre Holten, Daniel Holzinger, Karen Hopkins, Nina Jakhelln Laugen, Diane Lillo‐Martin, Lucas Magongwa, Amber Martin, Melissa McCarthy, Teresa McDonnell, Guita Movallali, Daiva Müllegger-Treciokaite, Stephanie C. Olson, Bolajoko O. Olusanya, Paula Pittman, Ann Porter, Jane M. Russell, Leeanne Seaver, Claudine Störbeck, Nanette Thompson, Sabine Windisch, Alys Young, Xuan Zheng

Bibliographic record

VenueThe Journal of Deaf Studies and Deaf Education · 2024
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
Fundersnot available
KeywordsIntervention (counseling)AudiologyPsychologyHearing lossDevelopmental psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

This article is the seventh in a series of eight articles that comprise a special issue on family-centered early intervention for children who are deaf or hard of hearing and their families, or FCEI-DHH. This article, Structure Principles, is the third of three articles (preceded by Foundation Principles and Support Principles) that describe the 10 FCEI-DHH Principles. The Structure Principles include 4 Principles (Principle 7, Principle 8, Principle 9, and Principle 10) that highlight (a) the importance of trained and effective Early Intervention (EI) Providers, (b) the need for FCEI-DHH teams to work collaboratively to support families, (c) the considerations for tracking children's progress through developmental assessment, and (d) the essential role of progress monitoring to continuously improve systems.

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 score0.916
Threshold uncertainty score0.268

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 head0.086
GPT teacher head0.358
Teacher spread0.273 · 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

Explore more

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