Delicate dances: immigrant workers' experiences of injury reporting and claim filing
Bibliographic record
Abstract
INTRODUCTION: Immigrants often come to Canada for the purpose of employment and make up a large proportion of our labour force. Yet, these workers' labour market experience may not always be positive - new immigrant workers can have difficulties finding a job in their field and may end up working in 'survival jobs' that expose them to workplace hazards. Workers who are new to Canada may not be familiar with legislation designed to protect them at work or with social programs that can help after a work-related injury. METHODS: Through a series of in-depth interviews this study examined the experiences of new immigrants after they were injured on the job. RESULTS: The analysis revealed that many workers were in manual, 'survival jobs' and had not received job or occupational health and safety training. Many did not speak the English language well and knew little about their rights. While workers often felt trepidation about reporting their injury, most told a health care provider or employer that they were injured or in pain. This, however, rarely led to timely or appropriate claim filing. Workers were often discouraged from filing a claim, misinformed about their rights or offered 'time off work' in lieu of reporting the injury to worker's compensation. In instances where a claim was filed, communication problems were common and led to mistakes being made on forms and misunderstandings with the adjudicator and employer. Interpretation services were not always offered consistently or at the correct time. CONCLUSION: Efforts must be made to systematically inform new immigrants of their health and safety rights, responsibilities and entitlements as they are entering the labour market. Systems must be put in place to ensure that immigrants can access the compensation system in the event of a work-related injury and that employers and healthcare providers fulfil their reporting responsibilities.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".