Towards a comprehensive approach for managing transitions of older workers with hearing loss
Bibliographic record
Abstract
Demographic and legislative trends suggest that many older workers may remain at work past the traditional retirement age. This extended work trajectory poses new challenges and opportunities for workers with acquired hearing loss as they age. Workplaces require a new approach to enable transitions of older workers with hearing loss to remain safe and productive. A review of the literature on older workers, those with hearing loss, and strategies used to accommodate them suggests that individualized and piecemeal approaches are predominant. While universal design represents a fresh ideology that may help create more accessible and usable products and environments, its application to improve workplaces for older workers with hearing loss is limited. This paper proposes that occupational science be integrated with knowledge in hearing sciences, accessibility, and usability to assist with the transitions faced by older workers with hearing loss. A more comprehensive approach including the following three key components will be posited to examine the nexus of aging, hearing loss and work: (1) the use of an occupational perspective, along with concepts in hearing sciences to examine hearing demands and improve hearing access; (2) the use of contextual processes to promote physical and social change, and (3) the inclusion of Universal Design for Hearing (UDH) considerations as stakeholders develop more hearing friendly workplaces.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".