O-378 CHARACTERISTICS INFLUENCING THE COMFORT OF EARPLUGS EXPERIENCED BY WORKERS IN CANADIAN COMPANIES
Bibliographic record
Abstract
Abstract Introduction In Quebec (Canada), noise-induced hearing loss is the most prevalent and growing occupational disease, resulting in higher compensation costs. Although the most effective solution is action at the noise source, the use of individual hearing protectors, such as earplugs, can be the only way to prevent this disease. Nevertheless, the various discomforts, encompassing acoustic, physical, functional, and psychological dimensions, that earplugs engender can undermine their efficacy by influencing their consistent and correct use. These (dis)comfort arises from intricate interplays between the characteristics of the user, earplugs, and the work environment, collectively constituting the concept of “triad”. Methods To better understand how the characteristics of the triad influence the earplugs (dis)comfort, a comprehensive study has been conducted. Results 173 workers tested seven distinct earplug models over the course of seven weeks and subsequently responded to detailed (dis)comfort questionnaires. Triad characteristics were assessed through both questionnaires and laboratory-based comfort testers. Statistical analyses led to the identification of primary triad characteristics that exert a significant influence on the acoustical, physical, and functional (dis)comfort dimensions. In this communication, highlights of the study and principal results are presented. Discussion and conclusion The main findings will aid manufacturers in designing better earplugs by considering not only their sound attenuation efficacy but also aspects related to perceived comfort. Furthermore, hygienists will have the ability to select earplugs that are most suited to a specific worker and his/her work environment.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".