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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".