THE EXPERIENCES OF HEALTH CARE WORKERS IN MANAGING AND PREVENTING COVID-19 AMONG MFWs IN NIAGARA REGION, ONTARIO
Bibliographic record
Abstract
Background: A significant proportion of Canada’s agricultural industry is employed by migrant farm workers. The Niagara Region of Ontario, Canada hosts some of the largest farming operations in the province and employs a large migrant workforce. The various challenges faced by these workers in Canada have long been recognized including barriers to healthcare accessibility. The COVID-19 pandemic threatened the health of many migrant farm workers in Ontario as the proximity in which workers live and work with one another lead to elevated infection transmission rates. At the time of study commencement, very little contemporary research had been conducted exploring health care for migrant farm workers amid the pandemic. The primary aim of this thesis is to accurately describe how COVID-19 has been managed among migrant workers in Niagara Region from the perspective of health care workers with an active role in health care provision and infection prevention. Methodology: A qualitative description study design with a naturalistic approach was used to capture a straightforward description of this novel phenomena. Seven health care workers employed at several different health organizations in Niagara Region were interviewed for this study using an open-ended interview guide. Results: Using qualitative content analysis, three themes and six categories were identified. The theme adapting to role changes identified the work role modifications experienced by participants in response to the COVID-19 pandemic. The ongoing implementation of and adjustment to regulations for COVID-19 prevention are discussed under the theme navigating regulation changes. The theme responsibility of healthcare services encompasses participant perspectives of how MFW and provider experiences are impacted by the nature of healthcare responsibility. Results from this study may inform recommendations for infectious disease programs geared toward migrant farm workers and help to identify areas for improvement regarding infection prevention practises in the workplace and living quarters.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".