Changing the narrative for hearing health in the broader context of healthy living: a call to action
Bibliographic record
Abstract
OBJECTIVE: To discuss the steps necessary to facilitate hearing health care in the context of well-being and healthy living. DESIGN: Common themes among the articles in this special supplement of the International Journal of Audiology were used to identify issues that must be addressed if audiology is to move from being hearing-focussed to taking a holistic perspective of hearing care in the context of healthy aging. These are discussed within the context of other published literature. RESULTS AND CONCLUSIONS: Three needs were identified: (i) Increased interdisciplinary education to raise awareness of the interplay between hearing and health. (ii) Increased emphasis on counselling education in audiology programs so that audiologists are equipped with the knowledge, competence and confidence to provide counselling and emotional support to their patients, beyond care. (iii) Redefinition of therapeutic goal setting and hearing outcomes to include aspects of well-being, so that audiologists can capture and patients realise that that good hearing outcomes can have a direct positive impact on a person's quality of life that extends beyond their improved ability to hear. It was emphasised that each of these needs to be considered within the context of the audiologists' scope of practice and audiologists' well-being.
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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 | 0.078 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.036 |
| Scholarly communication | 0.027 | 0.048 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.026 | 0.044 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".