The clinical use of personal hearing amplifiers in facilitating accessible patient–provider communication: A scoping review
Bibliographic record
Abstract
BACKGROUND: Older adults with hearing loss struggle to communicate with care providers and experience higher mortality rates when hospitalized (Genther et al., 2015), even after controlling for age and comorbidities. Personal hearing amplifiers (PHAs), (e.g., Pocketalkers™), can improve communication with older patients. METHODS: We conducted a scoping review to identify research gaps and summarize findings on the clinical use of PHAs with patients with hearing loss. After refining search terms relating to hearing loss and PHAs, we searched MEDLINE, Embase, PsycINFO, Cochrane Library, CINAHL Complete, Web of Science Core Collection, ERIC (Proquest), PubMed, ClinicalTrials.gov, International Clinical Trials Registry Platform, and the International Standard Randomised Controlled Trial Number Registry. We identified articles published in English between 1980 and 2022 that reported empirical outcomes relating to PHA use in clinical settings. Two reviewers independently extracted data from articles. We then organized data into an evidence map, and a narrative review summarizing outcomes. RESULTS: From 4234 initially identified titles and abstracts, 12 met our criteria as full texts. These included three surveys on clinicians' awareness and use of PHAs, one evaluation of the acoustic output of a PHA, and eight interventions wherein PHAs were provided to patients with hearing loss. These papers used 10 different terms for PHAs and largely did not cite one another. Results showed high levels of satisfaction with PHAs, and consistently improved speech understanding. Despite this, care providers used devices inconsistently, with challenges around provider awareness, and device maintenance and location tracking. CONCLUSIONS: PHAs have a consistent positive effect on patients' ability to understand their care providers despite hearing loss. Barriers and facilitators to their use in clinical settings should be further explored.
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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 | 0.016 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".