Assessing the comfort of earplugs: development and validation of the French version of the COPROD questionnaire
Bibliographic record
Abstract
Earplugs are a common form of protection for workers exposed to hazardous noise levels. Their comfort directly impacts the effective protection by influencing their consistent and correct use. Nevertheless, comfort definition may vary according to the studies. Thus, a previous review of the literature has shown that to improve our understanding of perceived comfort and to reduce measurement variability, it is advisable to consider comfort through a multidimensional construct (physical, acoustical, functional and psychological). On this basis, the COPROD (COnfort des PROtections auDitives/COmfort of hearing PROtection Devices) questionnaire was developed. It is intended for people working in noisy environments. Nine earplug models were evaluated by 118 participants over a six-week period. This paper presents the successive analyses that were used to validate the structure of the questionnaire and confirm the relevance of the proposed dimensions and of the addressed items. First results suggest a preference for custom moulded earplugs. Practitioner Summary: Earplugs comfort conditions the hearing protection of the users. As the definition of comfort can vary between studies, the COPROD questionnaire was developed to jointly evaluate all its dimensions. Nine earplugs models were evaluated by 118 participants during six weeks. This paper presents the validation process of the questionnaire. Abbreviations: COPROD: COnfort des PROtections auDitives/COmfort of hearing PROtection Devices; HPD: hearing protection devices; SEM: structural equation modeling; CFA: confirmatory factor analysis; GOF: goodness of fit; RMSEA: root mean square error of approximation; CFI: comparison fit index; SRMR: standardised root mean square residual
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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 | 0.001 | 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 | 0.000 |
| 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".