Hearing impairment, cognition and speech understanding: exploratory factor analyses of a comprehensive test battery for a group of hearing aid users, the n200 study
Bibliographic record
Abstract
OBJECTIVE: The aims of the current n200 study were to assess the structural relations between three classes of test variables (i.e. HEARING, COGNITION and aided speech-in-noise OUTCOMES) and to describe the theoretical implications of these relations for the Ease of Language Understanding (ELU) model. STUDY SAMPLE: Participants were 200 hard-of-hearing hearing-aid users, with a mean age of 60.8 years. Forty-three percent were females and the mean hearing threshold in the better ear was 37.4 dB HL. DESIGN: LEVEL1 factor analyses extracted one factor per test and/or cognitive function based on a priori conceptualizations. The more abstract LEVEL 2 factor analyses were performed separately for the three classes of test variables. RESULTS: The HEARING test variables resulted in two LEVEL 2 factors, which we labelled SENSITIVITY and TEMPORAL FINE STRUCTURE; the COGNITIVE variables in one COGNITION factor only, and OUTCOMES in two factors, NO CONTEXT and CONTEXT. COGNITION predicted the NO CONTEXT factor to a stronger extent than the CONTEXT outcome factor. TEMPORAL FINE STRUCTURE and SENSITIVITY were associated with COGNITION and all three contributed significantly and independently to especially the NO CONTEXT outcome scores (R(2) = 0.40). CONCLUSIONS: All LEVEL 2 factors are important theoretically as well as for clinical assessment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".