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Record W3024451179

Canadians vulnerable to workplace noise.

2018· article· en· W3024451179 on OpenAlexaffabout
Pamela L Ramage-Morin, Marc V. Gosselin

Bibliographic record

VenuePubMed · 2018
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsPublic Safety CanadaStatistics Canada
Fundersnot available
KeywordsLegislationTinnitusHearing lossOccupational safety and healthNoise (video)Industrial noiseMedicineEnvironmental healthWork (physics)Noise exposureHearing protectionAudiologyPsychologyPolitical scienceEngineeringLawComputer science
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Health and safety legislation is designed to protect workers from hazards, including excessive noise. However, some workers are not required to use hearing protection when exposed to loud noise and may be vulnerable to adverse outcomes, including hearing difficulties and tinnitus. DATA AND METHODS: Data for 19- to 79-year-olds (n=6,571) were collected from 2012 through 2015 as part of the Canadian Health Measures Survey. People exposed to loud workplace noise were defined as those who had to raise their voices to communicate at arm's length. Vulnerable workers were defined as those who were not required to use hearing protection when working in noisy environments and who only used hearing protection sometimes, rarely or never. RESULTS: An estimated 11 million Canadians (43%) have worked in noisy environments, and over 6 million of them (56%) were classified as vulnerable to workplace noise. Although the percentage of vulnerable women (72%) was greater than that of men (48%), men outnumbered women in these circumstances at 3.7 million, compared with 2.4 million. The self-employed were more likely than employees to be vulnerable, as were those in white-collar versus blue-collar occupations. Vulnerable workers were more likely to report hearing difficulties and tinnitus than those who had never worked in a noisy environment. DISCUSSION: A large percentage of workers exposed to noisy workplaces were vulnerable because hearing protection was neither required nor routinely used. Further work is required to assess whether this reflects gaps in health and safety legislation or its implementation.

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 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 score0.529
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.037
GPT teacher head0.335
Teacher spread0.298 · 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

Citations6
Published2018
Admission routes2
Has abstractyes

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