New Immigrant Workers and their Perspectives on the Occupational Health and Safety Aspects of their Jobs
Bibliographic record
Abstract
Background: New immigrants make an essential contribution to Canada’s economic and social development. They not only fill in the gap in the labor force arising from a decline in Canada’s working age population, but contribute to Canada’s economy by paying taxes, spending money on goods, housing and transportation. Yet new immigrants are one of most vulnerable sections of the Canadian society. Unable to gain entry into Canada’s strictly regulated professions and trades, several skilled and qualified new immigrants take up precarious jobs without adequate training, thereby increasing their risk for occupational injuries and illnesses when compared with native-born workers often doing the same job in the same industry. Methods: In-depth qualitative interviews (n= 40) were conducted with new immigrant workers from a range of industries operating in two cities in Alberta, to learn more about their work conditions. The data were analyzed using thematic analysis. Results: Findings reveal several safety concerns that the study participants had and the impact of these on their health and well- being. Conclusion: Based on the insights of study participants, policies and practices are proposed that will lead to improved occupational health and safety outcomes for new immigrant workers.
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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".