Acceptability of Audiovestibular Assessment in the Home—A Patient Survey
Bibliographic record
Abstract
The COVID-19 pandemic dramatically changed health service delivery with vulnerable patients advised to isolate and appointments provided virtually. This change affected recruitment into an observational cohort study, undertaken at a single site, where participants with mitochondrial disorders were due to have specialist hospital-based audiovestibular tests. To ensure study viability, the study protocol was amended to allow home-based assessment for vulnerable participants. Here, we report outcomes of an online survey of participants who underwent home-based assessment, related to the experience, perceived benefits, and drawbacks of home audiovestibular assessments. Seventeen participants underwent home-based neuro-otological assessment, due to the need to isolate during COVID-19. Following the assessment, 16 out of 17 participants completed an anonymised online survey to share their experiences of the specialist home-based assessment. One hundred percent of participants rated the home-based assessment 'very positively' and would recommend it to others. Sixty-three percent rated it better than attending hospital outpatient testing settings. The benefits included no travel burden (27%) and reduced stress (13%). A majority reported no drawbacks in having the home visit. The patient-reported feedback suggests a person-centred approach where audiovestibular assessments are conducted in their homes is feasible for patients, acceptable and seen as beneficial to a vulnerable group of patients.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".