User’s perspective of benefits of frequency-lowering hearing aids and electric acoustic stimulation cochlear implants in daily life
Bibliographic record
Abstract
BACKGROUND: Different technological alternatives are nowadays offered to persons with a severe-to-profound high-frequency hearing loss (HFHL). However, benefits of those technologies are still not clear. OBJECTIVE: To explore the benefits provided by frequency-compression (FC) or frequency-transpos ition (FT) hearing aids (HAs), and the electric acoustic stimulation (EAS) cochlear implant, from the perspective of users with a HFHL. METHODS: A qualitative case study research design was selected. Ten adults with a HFHL who participated in a previous FC, FT and EAS trial were enrolled. Individual semi-structured interviews were conducted. Participants were questioned about their experience with each technology. Data were analyzed using a qualitative content analysis. RESULTS: Participants reported better speech understanding in quiet and noisy situations, plus improved high-frequency sound detection with both HAs. Some participants mentioned lower levels of listening effort and fatigue and an improvement in self-confidence, which led to increased social participation. Most participants preferred FC or FT to their own HAs. The participant who received an EAS implant reported better performances with this technology. CONCLUSIONS: From the participants’ perspective, the three technologies can deliver greater benefits than conventional amplification for people with a severe-to-profound HFHL, but the EAS implant appears as potentially more beneficial than both HAs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".