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Record W4400075824 · doi:10.1093/annweh/wxae035.167

249a - Successful prevention strategies to prevent infectious airborne disease transmission in workers and our communities: a panel discussion

2024· article· en· W4400075824 on OpenAlexaboutno aff
Lawrence Sloan

Bibliographic record

VenueAnnals of Work Exposures and Health · 2024
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental healthTransmission (telecommunications)Disease transmissionInfectious disease (medical specialty)MedicineDiseaseVirologyTelecommunicationsComputer sciencePathology

Abstract

fetched live from OpenAlex

Abstract Introduction & Moderation of the Panel: COVID-19 is a preventable disease, primarily caused by the SARS-CoV-2 virus aerosols entering the respiratory system. There is no dose-response, no exposure limits, and no simple instrumental methods for detecting the virus. Since the virus travels with infected people, a primary challenge has been developing hazard/risk assessments and safe work procedures to prevent the virus from entering and exiting workplaces. Four senior Occupational, Environmental, Health, and Safety (OEHS) professionals will present successful prevention initiatives that lowered the risk of occupational transmission of SARS-CoV-2. These initiatives encompass the development and content of Canada’s new respiratory protection standards; quantitative respiratory protection fit testing for South African health care workers; COVID-19 as a preventable occupational disease in the Canadian film industry. The panel is moderated by the CEO of the AIHA, an IOHA member-association, who will discuss making representations for worker and community prevention to national and state government agencies, as well as allied professional associations.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.332

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 head0.074
GPT teacher head0.388
Teacher spread0.314 · 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
Published2024
Admission routes1
Has abstractyes

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