A review about hearing protection comfort and its evaluation
Bibliographic record
Abstract
Though it should ideally be the last choice in terms of noise exposure reduction, hearing protection devices (HPDs) remain the most commonly used noise control solution. However, the lack of comfort of HPDs can make it difficult for the worker to consistently and correctly wear them during work shift. It can thereby decrease their effective protection. Numerous studies have addressed the comfort of HPDs since the late fifties. These works mainly differ on (i) their definition of “comfort”, (ii) how “comfort” is measured (questionnaires), (iii) their measurement conditions (laboratory versus field, naïve versus experienced wearers, type of tested HPDs…) and finally, (iv) their conclusions. The objective of this paper is to propose a comprehensive literature review of these works and to put them into perspective regarding a definition of HPD comfort based on three main components: (1) the physical one which is related to the human perception of the acoustical, biomechanical and thermal interactions between the HPD and the ear, (2) the functional one which is associated to the ergonomic aspects of the HPD and its capacity to fulfill its objectives, and (3) the psychological one which is linked to the wearer feeling in terms of acceptability, satisfaction, or habituation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".