Usability, acceptability and clinical utility of a mobile app to screen for hearing loss in older adults in a geriatric rehabilitation hospital
Bibliographic record
Abstract
RATIONALE: Hearing loss is a common problem for older adults entering rehabilitation hospitals. AIMS AND OBJECTIVES: To pilot a hearing loss screening device to determine feasibility, usability, and impact on patient outcomes. METHODS: We screened all patients newly admitted to a geriatric day hospital for hearing loss using the SHOEBOX® QuickTest (SHOEBOX Ltd.) app as part of a quality improvement programme. We measured the time it took for each patient to complete screening and recorded any issues they had using the app. We recorded the number of patients who screened positive who did not have a previous diagnosis and changes in physician behaviours after they received their patients' results. RESULTS: Seventy-four patients with a mean age of 83.4 years used the hearing screener. All patients were able to complete the screening with a mean time to completion of 10 min and 48 s. Ninety-nine percent of patients screened positive for hearing loss. Of these positives 56% were in participants not already known to have hearing loss. Physicians often changed their behaviour after receiving results by using assistive devices during visits and referring to audiology for formal testing. CONCLUSIONS: Screening for hearing loss is feasible in a geriatric day hospital. The SHOEBOX QuickTest app is acceptable, usable, resulting in the identification of undiagnosed hearing loss and in changes to physician behaviour.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".