Functional Discomfort of Earplugs and its Influencing Variables
Bibliographic record
Abstract
Earplugs are extensively used as a preventive measure against noise-induced hearing loss. Nevertheless, their effectiveness is negatively impacted by the discomfort they induce, thereby influencing their consistent and proper use. In the context of earplug use, the comfort is defined as a multidimensional construct characterized by four dimensions: ‘physical’, ‘acoustical’, ‘functional’ and ‘psychological’. The (dis)comfort results from complex interactions between the triad constituted of the person wearing the earplug, his/her earplug, and his/her work environment. The objective of this paper is to enhance the understanding of the functional (dis)comfort induced by earplugs by identifying which triad characteristics can significantly influence the two attributes of this dimension: “functionality” (i.e., a general attribute) and “efficiency” (i.e., a specific attribute). Field assessments were conducted to evaluate the (dis)comfort of earplugs, involving 173 participants who tested different earplug models over seven weeks and provided a comprehensive comfort feedback through questionnaires. Triad characteristics were evaluated both on the field mainly through questionnaires, and in laboratory using comfort testers. Statistical analyses enabled the identification of key triad characteristics that influence functional comfort. Attributes of the functional comfort considered in this study are mainly governed by characteristics of the wearer: his/her previous experience with earplugs, his/her earcanal girth and a characteristic of the work environment: the interference of the earplug with other protective equipment The findings of this study hold the potential to provide manufacturers with tools for designing earplugs considering comfort aspects, as well as to help preventionists to recommend workers with earplugs that are best suited to individuals and work environment’s characteristics. © 2023, Canadian Acoustical Association. All rights reserved.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".