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Record W4393268457 · doi:10.1177/23312165241236041

A Longitudinal Framework to Describe the Relation Between Age-Related Hearing Loss and Social Isolation

2024· article· en· W4393268457 on OpenAlexafffund
Aysha Motala, Ingrid S. Johnsrude, Björn Herrmann

Bibliographic record

VenueTrends in Hearing · 2024
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsBaycrest HospitalUniversity of TorontoWestern University
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsHearing lossSocial isolationDisengagement theoryPsychologyPsychosocialActive listeningIsolation (microbiology)Context (archaeology)Developmental psychologyPsychological interventionCognitive psychologyAudiologyMedicineCommunicationGerontologyPsychiatry

Abstract

fetched live from OpenAlex

Many older adults live with some form of hearing loss and have difficulty understanding speech in the presence of background sound. Experiences resulting from such difficulties include increased listening effort and fatigue. Social interactions may become less appealing in the context of such experiences, and age-related hearing loss is associated with an increased risk of social isolation and associated negative psychosocial health outcomes. However, the precise relationship between age-related hearing loss and social isolation is not well described. Here, we review the literature and synthesize existing work from different domains to propose a framework with three conceptual anchor stages to describe the relation between hearing loss and social isolation: within-situation disengagement from listening, social withdrawal, and social isolation. We describe the distinct characteristics of each stage and suggest potential interventions to mitigate negative impacts of hearing loss on social lives and health. We close by outlining potential implications for researchers and clinicians.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.115
GPT teacher head0.360
Teacher spread0.245 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations16
Published2024
Admission routes2
Has abstractyes

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