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Record W2621612133 · doi:10.1121/1.4989269

A review about hearing protection comfort and its evaluation

2017· review· en· W2621612133 on OpenAlexaff
Olivier Doutres, Franck Sgard, Jonathan Terroir

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typereview
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailÉcole de Technologie Supérieure
Fundersnot available
KeywordsHearing protectionThermal comfortFeelingHabituationNoise (video)Computer scienceAudiologyPerceptionPerspective (graphical)Hearing lossApplied psychologyPsychologyMedicineSocial psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.245
GPT teacher head0.506
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicNoise Effects and ManagementFrench-language works237,207