Migrant agricultural workers’ deaths in Ontario from January 2020 to June 2021: a qualitative descriptive study
Bibliographic record
Abstract
BACKGROUND: Nine migrant agricultural workers died in Ontario, Canada, between January 2020 and June 2021. METHODS: To better understand the factors that contributed to the deaths of these migrant agricultural workers, we used a modified qualitative descriptive approach. A research team of clinical and academic experts reviewed coroner files of the nine deceased workers and undertook an accompanying media scan. A minimum of two reviewers read each file using a standardized data extraction tool. RESULTS: We identified four domains of risk, each of which encompassed various factors that likely exacerbated the risk of poor health outcomes: (1) recruitment and travel risks; (2) missed steps and substandard conditions of healthcare monitoring, quarantine, and isolation; (3) barriers to accessing healthcare; and (4) missing information and broader issues of concern. CONCLUSION: Migrant agricultural workers have been disproportionately harmed by the COVID-19 pandemic. Greater attention to the unique needs of this population is required to avoid further preventable deaths.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".