Participatory action research: A tool to develop occupational health and safety education for new immigrant workers
Bibliographic record
Abstract
Immigrant workers are a growing segment of Canada’s labour force, essential to its economy and society. Yet, they face occupational health and safety (OHS) challenges due to communication barriers, limited training, scarce resources, and workplace discrimination. This study sought to design learning resources to enhance the understanding of OHS in Canadian workplaces among new immigrant workers, drawing from their personal experiences. Through participatory action research, nine immigrant workers took part in six online sessions over three months, where they identified problems, discussed education, reflected, and decided on actions. Key issues raised included inadequate training, unawareness of OHS rights, hesitation in reporting safety concerns due to fear of backlash, and facing psychological threats like discrimination. This research illuminated the complex interplay of cultural and communication differences in OHS. Consequently, five education modules, rooted in real-world insights, were developed, emphasizing the significance of OHS, psychological risks, Canadian workplace norms, communication, and vital resources. This PAR successfully developed OHS learning modules, which not only tackle challenges and provide solutions for new immigrant workers but also craft with cultural sensitivity and lived expertise. These tools are tailored to equip new immigrant workers with the knowledge and confidence needed to enhance their OHS practices.
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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 | 0.109 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".