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Occupational Safety and Health in Small Businesses in Urban Areas: The Non-Participation of Immigrant Workers

2013· article· en· W2484139723 on OpenAlexaffabout
Sylvie Gravel, Gabrielle Legendre, Jacques Rhéaume

Bibliographic record

VenuePolicy and Practice in Health and Safety · 2013
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsOccupational safety and healthImmigrationBusinessSmall businessPublic relationsNursingEnvironmental healthMedicineMarketingPolitical scienceLaw

Abstract

fetched live from OpenAlex

An analysis of worker participation was carried out as part of a larger study on strategies for managing occupational safety and health in small businesses that hire immigrant workers. This analysis was based on the triangulation of three data sources: interviews with those who answered the questions on behalf of the small business owners or managers (n = 28); occupational health professionals who gave advice to the same small businesses (n = 26); and questionnaires completed by the immigrant workers (self-administered, n = 181).The results converged in that immigrant workers, compared to workers of Canadian origin, received less initial training when hired and were less able to identify risks. Immigrant workers informed their employer less often when they were injured and participated less in investigations following an accident. Many did not have protective equipment and, where the employer did provide it, they were less inclined to wear it. Generally, insufficient effort was made by small businesses to protect or inform immigrant workers of their rights and obligations, or to integrate them into the workplace. The study shows that it would be useful if company directors provided support to manage the occupational safety and health of immigrant workers and compliance with regulations, as well as endeavouring to understand the issues underlying equal labour and management representation in occupational safety and health.

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)0.007
metaresearch head score (Gemma)0.005
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 score0.389
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.115
GPT teacher head0.494
Teacher spread0.379 · 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

Citations10
Published2013
Admission routes2
Has abstractyes

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