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Record W4406023630 · doi:10.1080/14992027.2024.2442744

Tinnitus prevalence and associations with leisure noise exposure among Canadians, aged 6 to 79 years

2025· article· en· W4406023630 on OpenAlexaff
Katya Feder, Leonora Marro

Bibliographic record

VenueInternational Journal of Audiology · 2025
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversity of OttawaHealth Canada
Fundersnot available
KeywordsTinnitusNoise exposureAudiologyMedicineAssociation (psychology)Hearing lossGerontologyDemographyPsychology

Abstract

fetched live from OpenAlex

Objective To examine the association between individual, cumulative leisure noise exposure (CLNE), acceptable yearly exposure (AYE) and tinnitus among a nationally representative sample of Canadians.Design In-person household questionnaires were used to evaluate leisure noise exposure across age, sex, household income and tinnitus: ever experienced, previous year, frequent, bothersome. High (≥85 dBA, LEX), low (<85 dBA, LEX) CLNE and AYEs were defined according to occupational limits.Study sample A randomised sample of 10,460 respondents, aged 6–79, completed questionnaires between 2012 and 2015. Results: Tinnitus prevalence was highest among young adults and teenagers (50% for both). Frequent and bothersome tinnitus afflicted one-third and 18.1% of Canadians, respectively. Men had higher tinnitus prevalence while women had increased bothersome tinnitus. For most ages, elevated tinnitus odds ratios (ORs) were associated with: (1) high, low CLNE and AYEs ≥1 and, (2) high exposure from individual sources: loud home/car stereo listening, power tools, gasoline engines, highway motorcycle/snowmobile driving. Loud personal listening device usage was associated with tinnitus ORs doubling (ages 6–11) and ≥5 or <5 years of loud usage, with increased tinnitus ORs (ages 12–19).Conclusion Community and/or school-based educational outreach would be beneficial to increase awareness of loud leisure noise exposure and tinnitus.

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 score0.027
Threshold uncertainty score0.387

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 head0.012
GPT teacher head0.351
Teacher spread0.339 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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