Equity Lens on Canada’s COVID-19 Response: Review of the Literature
Bibliographic record
Abstract
BACKGROUND: A growing literature has documented how the secondary effects of the COVID-19 pandemic have compounded socioeconomic vulnerabilities already present in society, particularly across social categories such as gender, race, class, and socioeconomic status. Such effects demonstrate how pandemic response policies act as structural determinants of health to influence not only direct health outcomes but also intermediary outcomes, such as access to education or income. METHODS: This review aims to scope research that analyzes pandemic response policies in Canada from an equity perspective, to identify common themes, recommendations, and gaps. RESULTS: Fourteen studies were thematically analyzed, the majority being qualitative policy document analysis, applying critical frameworks and focused on effects on select priority populations. Analysis of economic and labour policies indicates a lack of consideration for the specific needs of priority populations, and those engaged in precarious, informal, and essential labour. Analysis of social policies illustrate the wide-ranging effects of school and service closures, particularly on women and children. Furthermore, these policies lacked consideration of populations marginalized during the pandemic, include older adults and their caregivers, as well as lack of consideration of the diversity of Indigenous communities. Recommendations proposed in this review call for developing policy responses that address persistent social and economic inequities, pandemic response policies tailored to the needs of priority populations and more meaningful consultation during policy development. CONCLUSION: The limited number of studies suggests there is still much scope for research recognizing policies as structural determinants of health inequities, including research which takes an intersectional approach.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.026 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".