Identity and COVID-19 in Canada: Gender, ethnicity, and minority status
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, growing evidence from the United States, the United Kingdom, and China has demonstrated the unequal social and economic burden of this health crisis. Yet, in Canada, studies assessing the socioeconomic and demographic determinants of COVID-19, and how these determinants vary by gender and ethnic minority status, remain scarce. As new strains of COVID-19 emerge, it is important to understand the disparities to be able to initiate policies and interventions that target and prioritise the most at-risk sub-populations. AIM: The objective of this study is to assess the socioeconomic and demographic factors associated with COVID-19-related symptoms in Canada, and how these determinants vary by identity factors including gender and visible minority status. METHODS: We implemented an online survey and collected a nationally representative sample of 2,829 individual responses. The original data collected via the SurveyMonkey platform were analysed using a cross-sectional study. The outcome variables were COVID-19-related symptoms among respondents and their household members. The exposure variables were socioeconomic and demographic factors including gender and ethnicity as well as age, province, minority status, level of education, total annual income in 2019, and number of household members. Descriptive statistics, chi-square tests, and multivariable logistic regression analyses were performed to test the associations. The results were presented as adjusted odds ratios (aORs) at p < 0.05 and a 95% confidence interval. RESULTS: We found that the odds of having COVID-19-related symptoms were higher among respondents who belong to mixed race [aOR = 2.77; CI = 1.18-6.48] and among those who lived in provinces other than Ontario and Quebec [aOR = 1.88; CI = 1.08-3.28]. There were no significant differences in COVID-19 symptoms between males and females, however, we did find a significant association between the province, ethnicity, and reported COVID-19 symptoms for female respondents but not for males. The likelihood of having COVID-19-related symptoms was also lower among respondents whose total income was $100,000 or more in 2019 [aOR = 0.18; CI = 0.07-0.45], and among those aged 45-64 [aOR = 0.63; CI = 0.41-0.98] and 65-84 [aOR = 0.42; CI = 0.28-0.64]. These latter associations were stronger among non-visible minorities. Among visible minorities, being black or of the mixed race and living in Alberta were associated with higher odds of COVID-19-related symptoms. CONCLUSION: We conclude that ethnicity, age, total income in 2019, and province were significantly associated with experiencing COVID-19 symptoms in Canada. The significance of these determinants varied by gender and minority status. Considering our findings, it will be prudent to have COVID-19 mitigation strategies including screening, testing, and other prevention policies targeted toward the vulnerable populations. These strategies should also be designed to be specific to each gender category and ethnic group, and to account for minority status.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".