Audiological approaches to address the psychosocial needs of adults with hearing loss: perceived benefit and likelihood of use
Bibliographic record
Abstract
Objective To explore the perceived benefit and likely implementation of approaches used by audiologists to address their adult clients’ psychosocial needs related to hearing loss.Design Adults with hearing loss and audiologists completed separate, but related, surveys to rate their perceived benefit and also their likely use of 66 clinical approaches (divided over seven themes) that aim to address psychosocial needs related to hearing loss.Study sample A sample of 52 Australian adults with hearing loss, and an international sample of 19 audiologists.Results Overall, participants rated all of the approaches highly on both benefit and likelihood of use; the highest ranked theme was Providing Emotional Support. Cohort comparisons showed that audiologists ranked the approaches significantly higher than did adults with hearing loss. Overall, participants ranked the themes higher on benefit than on the likelihood to use scales.Conclusions Adults with hearing loss and audiologists recognise the importance of approaches that address the psychosocial impacts of hearing loss in audiological rehabilitation. However, both groups placed slightly greater value on the internal-based approaches (the clients own emotional response, empowerment, and responsibility), and slightly less emphasis on the external-based approaches (being supported by communication partners, support groups or other health professionals).
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".