Cochlear implantation for a child with cochlear nerve deficiency: Parental perspectives explored through narrative
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".