P-576 ASSESSING THE MULTIDIMENSIONAL COMFORT OF EARPLUGS: VALIDATION OF THE NORTH AMERICAN COPROD QUESTIONNAIRE
Bibliographic record
Abstract
Abstract Introduction The use of hearing protection devices (HPDs), such as earplugs, is one way of reducing the risk of hearing loss associated with noise exposure. To ensure effective protection, workers must wear their earplugs consistently and properly, which is challenging since earplugs are often perceived as uncomfortable. Methods Based on a comfort model of HPD use, this contribution considers four comfort dimensions (physical, functional, acoustical, psychological) and the influence of the physical and psychosocial characteristics of the triad “environment/person/earplugs” (e.g., temperature, age, shape) on overall comfort. The purpose of this study was to validate the North American COmfort of hearing PROtection Device questionnaire (COPROD-NAQ) assessing the comfort dimensions of earplugs among people working in noisy environments. Results Longitudinal data were collected over a 7-week period in three Canadian manufacturing companies (N=173). Nine earplug models were tested. Participants answered to COPROD-NAQ as well as to another self-reported questionnaire measuring the characteristics of the triad. Factor analyses revealed that 51 items spread out on 11 conceptual sub-dimensions (e.g., acoustical comfort–internal noise; psychological comfort–habituation), with satisfactory alpha coefficients. Regression analyses showed that multiple characteristics of the triad (e.g., temperature, hand dominance, earplug model) influenced the overall comfort. Comparison tests showed that custom earplugs were significantly perceived as the most comfortable. Discussion and conclusion The COPROD-NAQ is a validated and powerful tool for assessing the multidimensional comfort of earplugs, and its interaction with the triad characteristics. It will be very useful in prevention programs and for helping manufacturers in designing more comfortable earplugs in order to reduce hearing loss among workers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".