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Record W3111387556 · doi:10.1080/19338244.2020.1854164

Noise exposure assessment of occupational health and safety (OHS) consultants: A preliminary study

2020· article· en· W3111387556 on OpenAlexaff
Chun‐Yip Hon, Jaskaren Randhawa, Craig Fairclough

Bibliographic record

VenueArchives of Environmental & Occupational Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsWorkplace Health, Safety and Compensation CommissionToronto Metropolitan University
Fundersnot available
KeywordsNoise exposureOccupational safety and healthNoise (video)Occupational exposureMedicineNoise-induced hearing lossHearing lossExposure assessmentConfidence intervalWeightingDosimeterEnvironmental healthAudiologyComputer scienceNuclear medicine

Abstract

fetched live from OpenAlex

No published noise exposure assessment of occupational health and safety (OHS) consultants exists. An assessment was performed to quantify the noise level of OHS consultants while they were on site at a client's facility. OHS consultants wore a dosimeter set to A-weighting, slow response, 60 s log interval and a criterion level of 85dBA with a 3 dB exchange rate. Both the projected time-weighted average (TWA) and projected dose were recorded. Of the 32 noise assessments collected, three had projected TWAs that exceeded the occupational exposure limit of 85 dBA. Nearly 75% of the projected TWA measurements were equal or greater than the Action Level of 80 dBA. According to best practices, occupational noise levels greater than 80 dBA present a risk for noise-induced hearing loss and, therefore, a hearing loss prevention program should be implemented.

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)0.003
metaresearch head score (Gemma)0.011
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 score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.047
GPT teacher head0.412
Teacher spread0.365 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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