Migrant Agricultural Workers’ Health, Safety and Access to Protections: A Descriptive Survey Identifying Structural Gaps and Vulnerabilities in the Interior of British Columbia, Canada
Bibliographic record
Abstract
= 179), we gathered information in three domains: (1) living and working conditions; (2) barriers to rights, health, safety and advocacy/reporting; (3) accessibility of services. Our study confirms what predominantly qualitative studies and Ontario-based survey data indicate in terms of health, legal, and social barriers to care and protection for this population. Our findings also highlight the prevalence of communication barriers and the limited degree of confidence in government authorities and contact with support organizations this population faces. Notably, survey respondents expressed a strong intention to report concerns/issues to authorities while simultaneously reporting that they lacked the knowledge to initiate such complaints. These findings call into question government responses that task the agricultural industry with addressing access and service gaps that may be more effectively addressed by government agencies and service providers. In order to improve supports and protections for migrant agricultural workers, policies and practices should be implemented that: (1) empower workers to independently access health, social, and legal protections and limit workers' dependence on their employers when help-seeking; (2) provide avenues for increased proactive inspections, anonymous reporting, alternative housing/employment and meaningful 2-way communication with regulators so that the burden of reporting is lessened for this workforce; (3) systematically address breaches in privacy, translation, and adequate workplace injury assessments in the healthcare system. Ultimately, the COVID-19 context has put into sharper focus the complex gaps in health, social and legal services and protections for migrant agricultural workers. The close chronology of our data collection with this event can help us understand the factors that have resulted in so much tragedy among this workforce.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".