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Record W4415785280 · doi:10.14639/0392-100x-n3075

Parent-child interaction and early pragmatic, auditory and linguistic abilities in deaf children

2025· article· en· W4415785280 on OpenAlexaff
Felicia Zagari, Giorgia Mari, Tiziana Di Cesare, Pasqualina Maria Picciotti, Ylenia Longobardi, Daniela Rodolico, Lucia D’Alatri

Bibliographic record

VenueActa Otorhinolaryngologica Italica · 2025
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsIntervention (counseling)Language developmentHearing lossAffect (linguistics)Hearing aid

Abstract

fetched live from OpenAlex

Objectives: To describe the Parent-Child Interaction (PCI) in prelingually deaf children with hearing aids and cochlear implants; to evaluate correlations between PCI, parental stress and family participation in the intervention programme, as well as between PCI and auditory and spoken language abilities. Methods: Twenty children (12 males, 8 females; mean age 21.8 ± 4.2 months) received a test battery including Categories of Auditory Performance (CAP), Social Conversational Skills Rating Scale for Assertiveness (SCS-A) and for Responsiveness (SCS-R), MacArthur-Bates Communicative Development Inventories (M-BCDI). PCI was assessed by video analysis, while parental stress and family participation were assessed with the Parenting Stress Index Short Form questionnaire (PSI) and Familiar Involvement Rate Scale (FIRS), respectively. Results: PCI style was "tutorial" in 15%, "modulated control" in 40%, "directive" in 20% and "asynchronous" in 25% of cases. A significant correlation was found between PCI and FIRS score, between PCI and CAP score, and between PCI and SCS-A rating scale score. Conclusions: Assessment of PCI in deaf children is important because it relates to family participation in the intervention programme and affects the development of auditory and pragmatic abilities.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.016
GPT teacher head0.317
Teacher spread0.301 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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