HEARING AID ACCLIMATIZATION BY OLDER ADULTS; THE EFFECT OF NOISE REDUCTION ON LISTENING EFFORT
Bibliographic record
Abstract
The objectives of this study is to investigate acclimatization of older adults (OA) listeners with hearing loss (HL) to hearing aids (HA) using listening effort measures with and without noise reduction algorithms (NRA). The dual-task paradigm was used to measure the effort to understand speech in noise. The primary task will be the Hearing In Noise Test (HINT). The HINT is an adaptive speech perception in noise test that identifies the Signal-to-Noise (SNR) necessary for a performance of 50%. The second task will be a tactile pattern-recognition task (TPRT) in which participants have to identify the three pulse combinations (i.e. short-short-short, short-short-long, etc.). There will be 8 testing sessions over a period of 16 months to measure the effect of acclimatization. The participants, aged between 60 and 75 years of age, will have a bilateral mild to moderately-severe sensorineural hearing loss. 30 participants will be new HA users (sub-divided in two groups; with NRA and without NRA) and the other 15 participants will be experienced hearing aid users who will be our control group. Cognitive skills, including working memory and the processing speed will be evaluated using the Reading Span Test (RST) and the Digit Symbol Substitution Test (DSST), respectively. Our hypotheses are that acclimatization as measured by listening effort will be significant for all new HA users and that it will be correlated with cognitive abilities. Moreover, we believe that the presence of NRA will extend the acclimatization period since it distorts the auditory signal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".