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Record W2050251361 · doi:10.3109/14992027.2013.820000

Cochlear implantation for a child with cochlear nerve deficiency: Parental perspectives explored through narrative

2013· article· en· W2050251361 on OpenAlexaff
Hannah Kotjan, Barbara Purves, Susan A. Small

Bibliographic record

VenueInternational Journal of Audiology · 2013
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsThematic analysisNarrativeContext (archaeology)InterviewPsychologyDevelopmental psychologyQualitative researchDaughterCochlear implantAudiologyMedicineSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this study was to explore, from the parents' perspectives, decision-making regarding a cochlear implant (CI) for their child when a favourable outcome is less likely because of abnormal neurophysiology. DESIGN: The primary research method of this single case study was qualitative interviewing drawing on a narrative approach to elicit the parents' perspectives about their experiences over time. Each parent was interviewed separately, but thematic analyses were undertaken both within and across interviews in order to identify overlaps and differences. STUDY SAMPLE: Participants included the parents of a five-year old child with severe-profound hearing loss, cochlear nerve deficiency, and bilateral common cavities who had received a CI at the age of 18 months. RESULTS: Four themes were identified across the four narrative stages that emerged from the parents' accounts of their experiences regarding their daughter's CI. Themes included hope and despair, questioning professionals' motivations, does deafness need a cure, and bringing the child into the family. Although these themes emerged from both parents' accounts, each parent expressed different perspectives and insights within them. CONCLUSIONS: Findings highlight the central place of parental needs and perspectives in decision-making regarding a CI, particularly in the context of uncertain outcomes.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.312
Teacher spread0.286 · 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 designBench or experimental
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

Citations7
Published2013
Admission routes1
Has abstractyes

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