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Record W4414050300 · doi:10.1055/s-0045-1811533

Auditory Training for Everyday Functioning in Later Life

2025· review· en· W4414050300 on OpenAlexaff
Karen Li, Jennifer L. Campos, M. Kathleen Pichora‐Fuller

Bibliographic record

VenueSeminars in Hearing · 2025
Typereview
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoConcordia University
Fundersnot available
KeywordsHuman multitaskingActive listeningEveryday lifeCognitionHearing lossRehabilitationCognitive training

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.122
GPT teacher head0.370
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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