Identifying the approaches used by audiologists to address the psychosocial needs of their adult clients
Bibliographic record
Abstract
OBJECTIVE: To identify the approaches taken by audiologists to address their adult clients' psychosocial needs related to hearing loss. DESIGN: A participatory mixed methods design. Participants generated statements describing the ways in which the psychosocial needs of their adult clients with hearing loss are addressed, and then grouped the statements into themes. Data were obtained using face-to-face and online structured questions. Concept mapping techniques were used to identify key concepts and to map each of the concepts relative to each other. STUDY SAMPLE: An international sample of 65 audiologists. RESULTS: Ninety-three statements were generated and grouped into seven conceptual clusters: Client Empowerment; Use of Strategies and Training to Personalise the Rehabilitation Program; Facilitating Peer and Other Professional Support; Providing Emotional Support; Improving Social Engagement with Technology; Including Communication Partners; and Promoting Client Responsibility. CONCLUSIONS: Audiologists employ a wide range of approaches in their attempt to address the psychosocial needs associated with hearing loss experienced by their adult clients. The approaches described were mostly informal and provided in a non-standardised way. The majority of approaches described were not evidence-based, despite the availability of several options that are evidence-based, thus highlighting the implementation gap between research and clinical practice.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".