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Record W4400355855 · doi:10.1093/occmed/kqae023.0045

SS01-01 THE SHEFFIELD GROUP AND ITS ROLE IN GLOBAL OSH RESEARCH ACTIVITIES

2024· article· en· W4400355855 on OpenAlexaboutno aff

Bibliographic record

VenueOccupational Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicMedicine and Dermatology Studies History
Canadian institutionsnot available
Fundersnot available
KeywordsGroup (periodic table)MedicineChemistry

Abstract

fetched live from OpenAlex

Abstract Introduction In 1988, a group of directors from national health and safety research facilities agreed that there would be value in creating a global network to discuss high level issues relating to the delivery of occupational health and safety research. The group held this first meeting in Sheffield (hence the “Sheffield Group”), and the Director of the UK facility was elected as the permanent Chair in perpetuity. The original membership included national labs from Australia, Canada, Denmark, France, Hungary, Norway, Netherlands, Poland, United Kingdom, Italy, Spain, Germany, Belgium, Russian Federation, Israel, Turkey, Finland, Czech Republic, Germany, Italy, Sweden and the USA. Materials and Methods Meetings of the Sheffield Group are held annually, hosted by one of the national labs. The purpose is to keep directors informed about programmes of research and national policy issues, and occasional collaborations across Institutes are also agreed. In 2003, PEROSH emerged from the Sheffield Group as a more formal mechanism to support joint research activities in Europe. Results The most recent meeting in Korea showcased the value of this network and provided insights which would not have been delivered through any other network. Conclusions The Sheffield Group is the only global network which supports directors of national occupational health and safety facilities. This not only allows a unique agenda of topics to be discussed but also helps to define why such national facilities are needed and the added value that they bring to national health and safety systems.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.312

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.066
GPT teacher head0.402
Teacher spread0.336 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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