An examination of clinical uptake factors for remote hearing aid support: a concept mapping study with audiologists
Bibliographic record
Abstract
OBJECTIVE: To develop a conceptual framework around the factors that influence audiologists in the clinical uptake of remote follow-up hearing aid support services. DESIGN: A purposive sample of 42 audiologists, stratified according to client-focus of either paediatric or adult, were recruited from professional associations in Ontario, Canada, as members of the six-step, participatory-based concept mapping process. Analyses included multidimensional scaling and hierarchical cluster analysis. RESULTS: Six main themes emerged from this research according to overall level of importance: (1) technology and infrastructure; (2) audiologist-centred considerations; (3) hearing healthcare regulations; (4) client-centred considerations; (5) clinical implementation considerations; and (6) financial considerations. Subthemes were identified at the group-level and by subgroup. These highlight the importance of TECH factors (accessible Technology, Easy to use, robust Connection, and Help available), as well as the multi-faceted nature of the perceived attitudes/aptitudes across stakeholders. CONCLUSION: Findings can be utilised in tailored planning and development efforts to support future research, knowledge dissemination, best-practice protocol/guideline development, and related training to assist in the clinical uptake of remote follow-up hearing aid support services, across variable practice contexts.
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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 | 0.032 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".