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Record W2136084489 · doi:10.1093/annhyg/men071

Comparison of Perceived and Quantitative Measures of Occupational Noise Exposure

2008· article· en· W2136084489 on OpenAlexaff
Richard L. Neitzel, William Daniell, Lianne Sheppard, Hugh Davies, Noah Seixas

Bibliographic record

VenueThe Annals of Occupational Hygiene · 2008
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOccupational exposureNoise exposureEnvironmental healthNoise (video)Occupational safety and healthOccupational medicineMedicineEnvironmental scienceAudiologyComputer sciencePathologyArtificial intelligence

Abstract

fetched live from OpenAlex

OBJECTIVES: Characterization of highly variable noise exposures over long periods of time presents a major challenge. Common exposure assessment strategies such as assignment of exposure levels based on job title information may not provide adequate exposure contrast or precision for variable exposures. Subjective exposure data may offer an alternative or complementary exposure assessment strategy. This study evaluated the relationship between perceived and quantitatively measured exposure. METHODS: Twenty subjects were recruited at each of three worksites with different noise environments (continuous, intermittent and highly variable). Full-shift quantitative measurements (n = 206) were made on each subject during four workshifts over 2 weeks. Perceived exposure data were collected via surveys on subjects' first (n = 58) and last (n = 57) monitored shifts, as well as through timeline logs completed by subjects during each monitored shift. The first survey focused on the first shift only, while the second survey covered the whole 2-week period. RESULTS: Timeline log data suggested that subjects could differentiate between different noise levels and degrees of noise variability. Survey items on perceived exposure variability and impulsiveness performed well at the continuous and highly variable sites. Analyses of contrast between exposure grouping strategies showed that job title generally did not produce statistically distinct exposure groups and that several survey items provided greater contrast than job title. The precision of exposures predicted from survey items was comparable to, or slightly better than, that of job title for several survey items, and the addition of survey items to prediction models which included job title improved model fit and precision. CONCLUSIONS: Supplemental perceived noise exposure information appears to offer promise for improving exposure estimates, particularly for individuals with highly variable exposures.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.420
GPT teacher head0.518
Teacher spread0.098 · 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 source (direct Gemma or distilled Codex), 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

Citations53
Published2008
Admission routes1
Has abstractyes

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