Analysis of the functional and acoustical comfort of earplugs experienced by a group of workers in Canadian companies and identification of the influencing variables
Bibliographic record
Abstract
Earplugs are essential for hearing protection in noisy workplaces, but their effectiveness depends heavily on user comfort, which influences proper and consistent use. This study explores functional and acoustical comfort experienced by 173 workers across Canadian companies, each testing different ’disposable or reusable’ earplug models over seven weeks. Comfort is assessed using detailed questionnaires covering six subdimensions: ease of insertion and removal, noise protection, impact on work, and discomfort related to internal and external noise perception. Linear mixed-effects models are applied within a triad framework encompassing person-, earplug-, and environment-related characteristics in order to identify those with a significant influence on functional and acoustical comfort. Results show that person-related variables are the most influential. Handedness, hearing loss, and prior HPD experience significantly impact comfort, with left-handed participants reporting greater insertion and removal discomfort—possibly due to earcanal asymmetry and dexterity differences. Several earcanal morphological features also play a role, including isoperimetric ratios, circumference at multiple cross-sections, conicity, and length. Only a few earplug-specific characteristics influence comfort outcomes. Foam expansion time is linked to reduced acoustical discomfort associated with the perception of internal sounds, while stem presence improves insertion ease. Environmental factors do not have significant effects. In the longer term, these findings call for a rethinking of the design and selection of ’disposable or reusable’ earplugs, primarily based on earcanal morphology and users’ past experience. The study also underscores the need for improved objective metrics to assess comfort and supports the development of more personalized hearing protection solutions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".