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Record W1969827847 · doi:10.1080/15459624.2012.680852

Survey of Noise Exposure and Background Noise in Call Centers Using Headphones

2012· article· en· W1969827847 on OpenAlexaff
Nicolas Trompette, Jacques Châtillon

Bibliographic record

VenueJournal of Occupational and Environmental Hygiene · 2012
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsHeadsetNoise exposureNoise (video)Industrial noiseHeadphonesAudiologyExposure assessmentPersonal protective equipmentEnvironmental healthOccupational exposureHearing protectionOccupational safety and healthQUIETMedicineEnvironmental scienceHearing lossTelecommunicationsComputer scienceEngineeringCoronavirus disease 2019 (COVID-19)Electrical engineering

Abstract

fetched live from OpenAlex

Call centers represent one of the fastest growing industries. However, there are health and safety hazards unique to this new industry. One of these potential hazards is hearing impairment caused by headsets. In this study, noise exposure assessment was performed at 21 call centers and for 117 operators. Although call center background noise does not contribute to noise exposure, it impacts working conditions and influences the headset volume setting. It was therefore measured at the same time as exposure to noise. Results revealed that although the risk of hearing impairment was generally low, exposure could exceed the European Union regulation upper and lower exposure action values. Besides exposure to noise, background noise levels are often high with regard to recommendations for office workers. Results are discussed and some recommendations are given, issued from on-site observations. Their application is intended to ensure the absence of excessive exposure to noise and improve acoustic comfort.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.055
GPT teacher head0.328
Teacher spread0.274 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations19
Published2012
Admission routes1
Has abstractyes

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