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Record W2163649601 · doi:10.1093/deafed/enh014

Hearing Mothers and Their Deaf Children: The Relationship between Early, Ongoing Mode Match and Subsequent Mental Health Functioning in Adolescence

2004· article· en· W2163649601 on OpenAlexaff
Delia Wallis

Bibliographic record

VenueThe Journal of Deaf Studies and Deaf Education · 2004
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyMental healthSign languageDevelopmental psychologyHearing lossModality (human–computer interaction)Clinical psychologyAudiologyPsychiatryLinguisticsMedicine

Abstract

fetched live from OpenAlex

In the few studies that have been conducted, researchers have typically found that deaf adolescents have more mental health difficulties than their hearing peers and that, within the deaf groups, those who use spoken language have better mental health functioning than those who use sign language. This study investigated the hypotheses that mental health functioning in adolescence is related to an early and consistent mode match between mother and child rather than to the child's use of speech or sign itself. Using a large existing 15-year longitudinal database on children and adolescents with severe and profound deafness, 57 adolescents of hearing parents were identified for whom data on language experience (the child's and the mother's) and mental health functioning (from a culturally and linguistically adapted form of the Achenbach Youth Self Report) was available. Three groups were identified: auditory/oral (A/O), sign match (SM), and sign mismatch (SMM). As hypothesized, no significant difference in mental health functioning was found between the A/O and SM groups, but a significant difference was found favoring a combined A/O and SM group over the SMM group. These results support the notion of the importance of an early and consistent mode match between deaf children and hearing mothers, regardless of communication modality.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.476

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 studies0.0010.000
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 head0.074
GPT teacher head0.369
Teacher spread0.295 · 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

Citations39
Published2004
Admission routes1
Has abstractyes

Explore more

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