Understanding the Challenges Experienced by Immigrant Personal Support Workers (PSWs) during the COVID-19 Pandemic: Evidence from Windsor, Ontario
Bibliographic record
Abstract
The COVID-19 pandemic had a substantial impact on the healthcare system globally, with healthcare workers (HCWs) facing several unprecedented challenges. Within the health workforce in Canada, personal support workers (PSWs) play an integral role, notably in providing care for vulnerable populations such as the elderly. The focus of this study was to understand the impact that stigma, labelling and intersectionality had on producing challenges among immigrant PSWs from Windsor, Ontario and surrounding areas. Drawing data from in-depth interviews (n=25), the findings demonstrated that immigrant PSWs faced many challenges during the pandemic, including being stigmatized, being treated poorly by employers and co-workers, workplace safety issues, financial concerns, and stress and anxiety related to family. Overall, the findings revealed that minorities face disproportionate disadvantages in times of crisis. Therefore, the findings from this study could be used to develop policies to better prepare minority groups such as immigrant PSWs for future pandemics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".