Evaluating auditory perception and communication demands required to carry out work tasks and complimentary hearing resources and skills for older workers with hearing loss
Bibliographic record
Abstract
For older workers with acquired hearing loss, this loss as well as the changing nature of work and the workforce, may lead to difficulties and disadvantages in obtaining and maintaining employment. Currently there are very few instruments that can assist workplaces, employers and workers to prepare for older workers with hearing loss or with the evaluation of auditory perception demands of work, especially those relevant to communication, and safety sensitive workplaces that require high levels of communication. This paper introduces key theoretical considerations that informed the development of a new framework, The Audiologic Ergonomic (AE) Framework to guide audiologists, work rehabilitation professionals and workers in developing tools to support the identification and evaluation of auditory perception demands in the workplace, the challenges to communication and the subsequent productivity and safety in the performance of work duties by older workers with hearing loss. The theoretical concepts underpinning this framework are discussed along with next steps in developing tools such as the Canadian Hearing Demands Tool (C-HearD Tool) in advancing approaches to evaluate auditory perception and communication demands in the workplace.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".