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Record W2588622922 · doi:10.1093/annweh/wxw018

Examining the Usefulness of ISO 10819 Anti-Vibration Glove Certification

2016· article· en· W2588622922 on OpenAlexaff
Diandra Budd, Ron House

Bibliographic record

VenueAnnals of Work Exposures and Health · 2016
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsStandardizationCertificationVibrationWork (physics)Transmissibility (structural dynamics)Computer scienceEngineeringRisk analysis (engineering)MedicineMechanical engineeringVibration isolationAcoustics

Abstract

fetched live from OpenAlex

Anti-vibration gloves are commonly worn to reduce hand-arm vibration exposure from work with hand-held vibrating tools when higher priority and more effective controls are unavailable. For gloves to be marketed as 'anti-vibration' they must meet the vibration transmissibility criteria described in the International Organization for Standardization (ISO) standard 10819 (2013). Several issues exist with respect to the methodology used for glove testing as well as the requirements for glove design and composition in ISO 10819 (2013). The true usefulness of anti-vibration gloves at preventing hand-arm vibration syndrome (HAVS) is controversial, given that their performance is dependent on tool vibration characteristics and the anthropometrics of workers in real working conditions. The major risk associated with the use of anti-vibration gloves is that it will give employees and employers a false sense of protection against the negative effects of hand-transmitted vibration. This commentary examines the limitations of the current international standards for anti-vibration glove testing and certification, thereby calling into question the degree of protection that anti-vibration gloves provide against HAVS, and cautioning users to consider both their benefits and potential drawbacks on a case-by-case basis.

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 score0.308
Threshold uncertainty score0.226

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 head0.218
GPT teacher head0.386
Teacher spread0.168 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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