Test Administration Methods and Cognitive Test Scores in Older Adults with Hearing Loss
Bibliographic record
Abstract
In light of the high prevalence of hearing loss and cognitive impairment in the aging population, it is important to know how cognitive tests should be administered for older adults with hearing loss. The purpose of the present study is to examine this question with a cognitive screening test and a working memory test. Specifically, we asked the following questions in 2 experiments. First, does a controlled amplification method affect cognitive test scores? Second, does test modality (visual vs. auditory) impact cognitive test scores? Three test administration conditions were created for both Montreal Cognitive Assessment (MoCA) and working memory test (a word recognition and recall test): auditory amplified, auditory unamplified, and visual. The auditory administration was implemented through a computer program to control for presentation level while the visual condition was implemented through timed computer slides. Data were collected from older individuals with mild-to-moderate sensorineural hearing loss. We did not find any effect of amplification or test modality on the total score of the cognitive screening test (i.e., MoCA). Amplification improved working memory performance as measured by word recall performance, but test modality (auditory vs. visual) did not. These results are consistent with literature in demonstrating a downstream effect of audibility on working memory performance. From a clinical perspective, these findings are informative for developing clinical administration protocols of these tests for older individuals with hearing loss.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".