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Record W2763136728 · doi:10.1093/deafed/enx043

Literacy Outcomes in Deaf Students with Cochlear Implants: Current State of the Knowledge

2017· review· en· W2763136728 on OpenAlexaff
Connie Mayer, Beverly J. Trezek

Bibliographic record

VenueThe Journal of Deaf Studies and Deaf Education · 2017
Typereview
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsYork University
Fundersnot available
KeywordsPsychologyReading (process)LiteracyCochlear implantDevelopmental psychologyReading comprehensionComprehensionConsistency (knowledge bases)AudiologyPedagogyMedicineLinguistics

Abstract

fetched live from OpenAlex

The purpose of this paper is to examine the available peer-reviewed research regarding literacy achievement in deaf children with cochlear implants. A related goal is to identify gaps in the empirical literature and suggest directions for future research. Included in this review are studies that exclusively report reading and writing outcomes for groups of students. A total of 21 studies were identified, representing those published over approximately a 20-year time period (1997-2016) and collectively reporting the literacy outcomes for over 1,000 cochlear implant users. Overall findings of the studies of reading comprehension suggest that the majority of participants achieved scores in the average range, although a wide-range of variability was reported. Only 3 studies of writing were available for review, with 2 reporting lower achievement in this area as compared to reading. Factors impacting outcomes achieved such as age at implantation, presence of additional disabilities, consistency of device use, and communication modality are explored and summarized.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.172
GPT teacher head0.550
Teacher spread0.377 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations135
Published2017
Admission routes1
Has abstractyes

Explore more

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