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Record W2980839722 · doi:10.1177/1048291119876681

Occupational Health and Safety and the Mobile Workforce: Insights From a Canadian Research Program

2019· article· en· W2980839722 on OpenAlexafffundabout
Barbara Neis, Katherine Lippel

Bibliographic record

VenueNEW SOLUTIONS A Journal of Environmental and Occupational Health Policy · 2019
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of OttawaMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWorkforceWork (physics)Context (archaeology)Occupational safety and healthBusinessPolitical scienceEconomic growthEngineeringGeographyEconomics

Abstract

fetched live from OpenAlex

Globally, employment-related geographical mobility (mobility to and within work) is a pervasive aspect of work that has potential health and safety implications. As an introduction to this special issue, this article defines the mobile workforce as those who engage in complex/extended mobility to and within work encompassing >two hours daily, less frequent but more extended mobility between regions and countries, and mobility within work such as between work sites or in mobile workplaces. Focusing on the Canadian context, we discuss the challenges associated with developing a statistical profile for this diversely mobile workforce and provide an overview of articles in the special issue identifying key health and safety challenges associated with extended/complex employment-related geographical mobility. We estimate that up to 16 percent of Canada’s employed labor force (including those commuting > one hour one-way, temporary residents with work permits, and transportation workers) engage in extended/complex mobility related to work.

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)0.007
metaresearch head score (Gemma)0.013
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 score0.116
Threshold uncertainty score0.844

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.014
Science and technology studies0.0130.003
Scholarly communication0.0060.003
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.090
GPT teacher head0.480
Teacher spread0.391 · 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

Citations17
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueNEW SOLUTIONS A Journal of Environmental and Occupational Health PolicySame topicOccupational Health and Safety ResearchFrench-language works237,207