Age, vision loss, and audiobooks: Experiences of the transition to a new medium
Bibliographic record
Abstract
Vision loss in later life can disrupt long-held reading habits and deeply personal relationships with stories, which remain vital for well-being. Audiobooks present an alternative medium to engage with narratives for immersive and pleasurable experiences. Yet, how older adults with vision loss experience and adapt to a medium with which they are not familiar is not well understood. This article reports the findings of a qualitative study on the experiences of older people with vision loss (> 60 years) listening to audiobooks. Drawing on in-depth interviews, the findings highlight three interrelated themes grounded in participants’ lived experiences: (1) Adaptation, illustrating how vision loss marked a pivotal moment in participants’ reading lives; (2) Learning to listen, detailing the process of becoming immersed in audiobooks through new sensory and attentional strategies; and (3) Building connections, revealing how listening fostered both social engagement and companionship. These insights reveal that audiobooks serve a fundamental role in the lives of older adults with vision loss and offer opportunities for cognitive engagement, social connection, and emotional well-being. Our results further concretize barriers related to technological adaptation and accessibility, and encourage discussions in gerontology of how inclusive design, support, and policy can facilitate the affective and social dimensions of later-life sensory loss.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".