Quality of Life and Satisfaction among Patients Who Use Hearing Aids
Bibliographic record
Abstract
BACKGROUND: Hearing loss is a very common condition, especially among the elderly. A large number of people that have disabling hearing loss may benefit from hearing aids.AIM: The purpose of this study was to measure quality of life and satisfaction among patients who use hearing aids.METHODS: A cross-sectional study was conducted, in which 100 patients who came in contact with an audiology center in Athens took part. Glasgow Hearing Aid Benefit Profile (GHABP) and 12-Item Short Form Health Survey (SF-12) were used. Alongside with the questionnaires, demographic and relevant to their hearing aid information were collected. Student's t-test, Pearson correlation and Linear regression analysis with the sequential process of integration/abstraction to find independent factors associated with the various scales that generated dependency coefficients (b) and their standard errors (SE), were used.RESULTS: Patient's perception of disability affects use of hearing aid and patients' satisfaction. More specifically patients who used hearing aid more and perceived its benefits showed higher scores in the physical health of SF-12. Age was found to correlate statistically with patients' satisfaction from the hearing aid and decreases as age increases. Also greater subjective perception of disability caused by hearing loss means increased usage of a hearing aid and life satisfaction.CONCLUSION: Using a hearing aid improves physical aspects of quality of life of patients with hearing loss. The total duration of wearing a hearing aid and the degree of hearing loss play an important role in the use made by the patient.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".