Public-engagement strategies of the South Asian COVID-19 Task Force: The role of racialized healthcare workers in COVID-19 mitigation in Ontario
Bibliographic record
Abstract
The COVID-19 pandemic began in late 2019 and its uneven impact across different communities globally was quickly evident. In Canada, South Asian communities were disproportionately affected. In response, the South Asian COVID-19 Task Force (SACTF) emerged, seeking to address the unique challenges faced by the South Asian community. The embedded single case study design was employed to explore the role of SACTF in COVID-19 mitigation in Ontario. Informed by critical race theory and a public engagement conceptual framework published by the Canadian Health Services Research Foundation (2010), we analyzed how contexts guided the goals, processes, and outcomes of SACTF activities. We conducted one-on-one semi-structured interviews and focus group discussions with SACTF's Board of Directors and analyzed SACTF-produced knowledge dissemination materials and media coverage of SACTF spanning March 2020 to February 2022. SACTF's success in educating and advocating for South Asians offers important insights into the gaps in public health communication and the inequities in healthcare delivery. It emphasizes the importance of tailoring emergency responses to community-specific needs and the role of racialized healthcare workers in facilitating trust-building within minority communities. By incorporating insights of racialized healthcare workers in health system decision-making, both public engagement and community health outcomes can be improved. This study contributes to a nuanced understanding of community-centric pandemic responses and demonstrates the need for diverse representation in decision-making processes for long-term health system resilience. Both healthcare knowledge and lived experiences made SACTF alert to how pandemics unfold differently and have differential effects on racialized populations. SACTF's responses offer practical recommendations for future pandemic preparedness and emergency responses, emphasizing the role of advocacy groups in addressing public health gaps and serving as crucial allies for communities and governments.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".