Auditory Training for Everyday Functioning in Later Life
Bibliographic record
Abstract
Following from the World Health Organization's consideration of multiple systems (e.g., sensory, motor, and cognitive) in defining healthy aging, this study presents a review of research on training that has the primary goal of improving complex multitasking outcomes that approximate the everyday contexts in which hearing is important, whether or not older adults are living with clinically significant audiometric hearing loss. Background on the interplay between sensory, motor, and cognitive systems establishes the rationale for considering complex listening behaviors as primary outcomes, and for focusing training on domain-free executive function (EF) processes such as selection, inhibition, and working memory updating. Approaches to cognitive training in later life are discussed to provide a foundation for a deeper examination of targeted EF training and complex listening outcomes that reflect performance in everyday activities. Where available, studies involving older adults with hearing loss are included, although many studies include a mixture of older adults with good audiograms, sub-clinical audiometric loss, or clinically significant but untreated audiometric loss. Overall, the reviewed literature suggests that older adults, with or without audiometric hearing loss, can benefit from EF training that improves complex listening performance. Future clinical considerations are discussed, including rehabilitation that extends from communication training to realistic multitasking training.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".