Effective communication as a fundamental aspect of active aging and well-being: paying attention to the challenges older adults face in noisy environments
Bibliographic record
Abstract
Successful communication is vital to active aging and well-being, yet virtually all older adults find it challenging to communicate effectively in noisy environments. The resulting discomfort and frustration can prompt withdrawal or avoidance of social situations, which, in turn, can severely limit the range of activities available to older adults and lead to a less active and satisfying lifestyle, and, in some cases, depression. Using the International Classification of Functioning, Disability and Health’s (ICF) multifactorial model (WHO, 2001), we review the wider aspects of functioning and disability as they relate to hearing difficulties and communication, placing a particular emphasis on the work we, an international and interdisciplinary group of researchers, have done in the context of the ERA-NET funded interdisciplinary HEARATTN project. The ICF model is particularly fitting because it allows us to consider how physiological changes in hearing and cognition affect listening in various situations, what the consequences of these changes are for communicative abilities and social participation, and how this in turn affects life-space mobility, self-reported well-being, and, ultimately, quality of life. We will discuss how environmental conditions (both physical and social) and personal factors can affect how well older adults can communicate in the situations characteristic of everyday life. In the concluding section we discuss some behaviors, techniques and strategies that can be adopted to maintain or improve effective communication under difficult listening conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".