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Record W144958791 · doi:10.26443/mjm.v11i2.490

Loud Music Listening

2020· article· en· W144958791 on OpenAlexaffvenue
Nicolae Petrescu

Bibliographic record

VenueMcGill Journal of Medicine · 2020
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHearing lossEntertainmentActive listeningMedicineLimitingAudiologyHearing protectionPsychologyVisual artsCommunication

Abstract

fetched live from OpenAlex

Over the past four decades, there has been increasing interest in the effects of music listening on hearing. The purpose of this paper is to review published studies that detail the noise levels, the potential effects (e.g. noise-induced hearing loss), and the perceptions of those affected by music exposure in occupational and non-occupational settings. The review employed Medline, PubMed, PsychINFO, and the World Wide Web to find relevant studies in the scientific literature. Considered in this review are 43 studies concerning the currently most significant occupational sources of high-intensity music: rock and pop music playing and employment at music venues, as well as the most significant sources of non-occupational high-intensity music: concerts, dicotheques (clubs), and personal music players. Although all of the activities listed above have the potential for hearing damage, the most serious threat to hearing comes from prolonged exposures to amplified live music (concerts). The review concludes that more research is needed to clarify the hearing loss risks of music exposure from personal music players and that current scientific literature clearly recognizes an unmet hearing health need for more education regarding the risks of loud music exposure and the benefits of wearing hearing protection, for more hearing protection use by those at risk, and for more regulations limiting music intensity levels at music entertainment venues.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.734
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.136
GPT teacher head0.413
Teacher spread0.276 · 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.

Study designNot applicable
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

Citations22
Published2020
Admission routes2
Has abstractyes

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