Qualitative case studies of five cochlear implant recipients' experience with music
Bibliographic record
Abstract
Cochlear implantation has revolutionized the management of those who suffer from severe-to-profound hearing loss because many patients now achieve excellent speech understanding with objective testing. Nevertheless, speech understanding in noisy conditions and music appreciation remain significant challenges to cochlear implant (CI) users. Music appreciation is an extremely complex experience that is difficult to quantify through a conventional outcome study. This paper aims at documenting the experience of five CI patients with regard to music appreciation using qualitative techniques. This information was obtained through a semi-structured interview process. The interviews were then transcribed and analysed using a constant comparative method of qualitative description. The results together with medical case records were used to identify emerging themes. The common themes that evolved were: musical background, the experience of receiving the implant, current experience with music, attention, musical prediction ability, internal hearing, hedonic vs. critical listening, determination, and timbre perception. By documenting their experiences in this manner, novel insights into the patient perspective are provided that are unique to the literature. These descriptions will aid clinicians and researchers who work in the area of cochlear implantation to better understand the needs of their patients.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".