Impacts and Identification of Hearing Aid Refurbishing Programs for People with Hearing Loss: A Scoping Review
Bibliographic record
Abstract
This article consists of a scoping review completed to describe the impacts of refurbished hearing aids (HAs) for people with hearing loss, and to identify existing HA refurbishing programs around the world. In this review, JBI methodological guidance for scoping reviews was followed. All types of sources of evidence were considered. Thirty-six sources of evidence were included, 11 articles and 25 web pages. Results suggest that refurbished HAs may improve communication and social participation for individuals with hearing loss and provide monetary savings to them and to governmental agencies. Twenty-five HA refurbishing programs were identified, all based in developed countries and distributing refurbished HAs mostly locally, but also in developing countries. Issues related to refurbished HAs were highlighted, such as potential cross-contamination, quick obsolescence, and repairing problems. Some facilitators for the success of this intervention are to offer accessible and affordable follow-up services, repairs, and batteries, and to ensure awareness and participation of hearing healthcare professionals and citizens with hearing loss. In conclusion, the use of refurbished HAs appears to be a valuable option for low-income people with hearing loss, but it should be included in a more global intervention program to ensure its sustainability.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".