Cochlear Implant Decision Making for Children With Residual Hearing: Perspectives of Practitioners
Bibliographic record
Abstract
PURPOSE: Cochlear implants (CIs) are increasingly considered for children with residual hearing who benefit from hearing aids (HAs). However, the decision-making process for families of these children and for practitioners is particularly challenging because there is no clear audiological cut point for CI candidacy. This study aimed to understand Canadian practitioners' perspectives of the CI decision-making process and how they guide families of children with residual hearing. METHOD: Semistructured interviews were conducted with a total of 17 practitioners through four focus groups and one individual interview. Interviews were transcribed verbatim, and a thematic analysis was carried out. RESULTS: Data were organized into five broad domains: candidacy issues for children with residual hearing, practitioners' roles in decision support, additional considerations affecting decision making, factors facilitating decision making, and practitioners' needs. CONCLUSIONS: This study found that practitioners' confidence in determining candidacy and supporting parents has increased due to their experiences with positive outcomes for these children. Practitioners indicated that there was a need for more research to guide the decision-making process.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".