Vulnerabilities and Capacities of Filipino Workers Quarantining During Workplace COVID-19 Outbreaks in Cargill, Alberta
Bibliographic record
Abstract
Background: Enacting public health measures were critical to control the spread of COVID-19 and to protect those at high risk for serious health outcomes. According to the State of Public Health in Canada 2020, the many impacts of COVID-19 can be examined as being direct (i.e., from infection with SARS-CoV-2) and indirect, (i.e., those arising from the public health measures). This study aims to explore the impact of quarantining of Filipino workers during COVID-19 outbreaks in Cargill, AB. It will examine at how the community demonstrates resiliency by identifying their capacities and vulnerabilities that were amplified. Methods: We will conduct a semi-structured interview with the Filipino workers who were quarantining during the COVID-19 outbreak in Cargill meat processing plant. We will use the Capacities and Vulnerabilities Analysis Framework as a guide to analyze the data by deductive thematic method. Expected results: This study will reveal the vulnerabilities and capacities of the community in terms of their physical, social and motivational domains. Conclusion: This study hopes to find sustainable solutions to better meet the needs of workers during quarantining and future high scale health risk exposure. It will inform the policy decision makers in both the public health and business sectors in designing more responsive public health measures in preventing future outbreaks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".