Pushing for Structural Reforms : Impacts of Racism and Xenophobia upon the Health of South Asian Communities in Ontario, Canada
Bibliographic record
Abstract
South Asian (SA) communities in Ontario, Canada experienced disproportionately higher rates of COVID-19 infection. Moreover, these communities also faced racism fueled by COVID-19-related misinformation and xenophobic sentiments that placed blame on them for virus transmission. The aim of this research was to understand, from the perspective of local SA communities, the causes behind higher incidences of COVID-19. SA adults (N = 25) participated in a focus group (N = 3) investigating experiences during the early stages of the pandemic. Data, interpreted through the lens of the Public Health Critical Race Praxis, suggest that the structural determinants of health, alongside racism and xenophobia, negatively impacted health outcomes for these communities. By not taking an active anti-racist stance, media, health and government authorities were viewed as perpetuating discriminatory narratives and practices, fueling blame and stigma towards these South Asian communities for COVID-19 transmission. Local public health policies, practices and communications were perceived to be informed by, and best serve, white Anglo-European settlers. This research provides insight into the role that health officials can play in addressing local and regional discrimination and stigma to promote equity-centered disease prevention efforts. Our findings should be integral to current and ongoing research and action related to pandemic preparedness.
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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 | 0.017 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".