Socio-cultural correlates of self-reported experiences of discrimination related to COVID-19 in a culturally diverse sample of Canadian adults
Bibliographic record
Abstract
Minorities and marginalized groups have increasingly become the target of discriminatory actions related to the COVID-19 pandemic. Detailed information about the manifestation of COVID-related discrimination is required to develop preventive actions that are not stigmatizing for such groups. The present study investigates experiences of perceived discrimination related to COVID-19 and its socio-cultural correlates in a culturally diverse sample of adults in Quebec (Canada). An online survey was completed by 3273 Quebec residents (49 % 18-39 years old; 57 % female; 49 % White). We used multivariate binomial logistic regression models to assess prevalence of COVID-related discrimination and to investigate socio-cultural correlates of reasons and contexts of discrimination. COVID-related discrimination was reported by 16.58 % of participants. Non-white participants, health-care workers and younger participants were more likely to experience discrimination than White, unemployed and older participants, respectively. Discrimination was reported primarily in association with participants' ethno-cultural group, age, occupation and physical health and in the context of public spaces. Participants of East-Asian descent and essential workers were more likely to report discrimination because of their ethnicity and occupation, respectively. Although young people experienced discrimination across more contexts, older participants were primarily discriminated in the context of grocery stores and because of their age. Our findings indicate that health communication actions informed by a social pedagogy approach should target public beliefs related to the association of COVID-19 with ethnicity, age and occupation, to minimize pandemic-related discrimination. Visible minorities, health-care workers and seniors should be protected and supported, especially in public spaces.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".