Occupational Safety and Health in Small Businesses in Urban Areas: The Non-Participation of Immigrant Workers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".