Noise sensitivity or hyperacusis? Comparing the Weinstein and Khalfa questionnaires in a community and a clinical samples
Bibliographic record
Abstract
Noise sensitivity and hyperacusis are decreased sound tolerance conditions that are not well delineated or defined. This paper presents the correlations and distributions of the Noise Sensitivity Scale (NSS) and the Hyperacusis Questionnaire (HQ) scores in two distinct large samples. In Study 1, a community-based sample of young healthy adults (n = 103) exhibited a strong correlation (r = 0.74) between the two questionnaires. The mean NSS and HQ scores were 54.4 ± 16.9 and 12.5 ± 7.5, respectively. NSS scores displayed a normal distribution, whereas HQ scores showed a slight positive skew. In Study 2, a clinical sample of Veterans with or without clinical comorbidities (n = 95) showed a moderate correlation (r = 0.58) between the two questionnaires. The mean scores were 66.6 ± 15.6 and 15.3 ± 7.3 on the NSS and HQ, respectively. Both questionnaires' scores followed a normal distribution. In both samples, participants who self-identified as having decreased sound tolerance scored higher on both questionnaires. These findings provide reference data from two diverse sample groups. The moderate to strong correlations observed in both studies suggest a significant overlap between noise sensitivity and hyperacusis. The results underscore that NSS and HQ should not be used interchangeably, as they aim to measure distinct constructs, however to what extent they actually do remains to be determined. Further investigation should distinguish between these conditions through a comprehensive psychometric analysis of the questionnaires and a thorough exploration of psychoacoustic, neurological, and physiological differences that set them apart.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".