Systemic barriers to reporting work injuries and illnesses in contexts of language barriers
Bibliographic record
Abstract
BACKGROUND: Workers who experience language barriers are at increased risk of work-related injuries and illnesses and face difficulties reporting these health problems to their employer and workers' compensation. In the existing occupational health and safety literature, however, such challenges are often framed in individual-level terms. We identify systemic barriers to reporting among injured workers who experience language barriers within the varying contexts of Ontario and Quebec, Canada. METHODS: This study merges data from two qualitative studies that investigated experiences with workers' compensation and return-to-work, respectively, for injured workers who experience language barriers. We conducted semi-structured interviews with 39 workers and 70 stakeholders in Ontario and Quebec. Audio recordings were transcribed and coded using NVivo software. The data was analysed thematically and iteratively. RESULTS: Almost all workers (34/39) had filed a claim, though most had initially delayed reporting their injuries or illnesses to their employer or to workers' compensation. Workers faced several obstacles to reporting, including confusion surrounding the cause and severity of injuries and illnesses; lack of information, misinformation, and disinformation about workers' compensation; difficulties accessing and interacting with care providers; fear and insecurity linked to precarity; claim suppression by employers; negative perceptions of, and experiences with, workers' compensation; and lack of supports. Language barriers amplified each of these difficulties, resulting in significant negative impacts in economic, health, and claim areas. CONCLUSION: Improving the linguistic and cultural competence of organizations and their representatives is insufficient to address under-reporting among workers who experience language barriers. Efforts to improve timely reporting must tackle the policies and practices that motivate and enable under-reporting for workers, physicians, and employers.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".