Impact of hearing loss in the workplace: Raising questions about partnerships with professionals
Bibliographic record
Abstract
The number of adults with hearing loss who continue to work later in life is growing. Persons with hearing loss are generally unaware of the role that audiologists, occupational therapists, and vocational rehabilitation counsellors might play in the assessment of the workplace environment and appropriate accommodations. Three narratives of adults with hearing loss are used to demonstrate the gaps in accessing information, technology and services needed to maintain optimal work performance and productivity. The lack of recognition of the multidimensional needs of older workers with hearing loss and the lack of timely coordination of services led to all three persons acting alone in trying to access services and supports. In two of the three cases the impact of the hearing loss resulted in further unexpected losses such as the loss of employment and the loss of a worker-identity. There is an urgent need for partnering with persons who are hard of hearing to develop new strategies for knowledge exchange, more thorough assessment of hearing demands and modifications in the workplace, and interdisciplinary approaches to service specific to the needs of hard of hearing persons.
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 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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".