A scoping review of intersectional health research related to the COVID-19 pandemic in North America: key findings
Bibliographic record
Abstract
BACKGROUND: This scoping review maps the key findings of intersectional research related to the COVID-19 pandemic in North America. Intersectional approaches highlight how overlapping systems of oppression shape health and social outcomes. METHODS: A total of 21 studies were included, comprising 10 quantitative, 8 qualitative, and 3 mixed-methods designs. Studies were reviewed to assess the use of intersectional research methods and to identify common findings across the literature. RESULTS: Intersectional research methods are increasingly utilized in pandemic-related studies in North America. Thematic analysis revealed five key themes: deepening disparities in health care systems, barriers to accessing social services, changes to working conditions across economic sectors, impacts of lockdown restrictions, and impacts on mental health. This review also found that interruptions to community connections influenced access to resources, shaping life chances for some populations. Importantly, intersectional research related to the pandemic has often decentralized race, which contrasts with broader non-intersectional studies. CONCLUSIONS: Findings underscore the need for public health policies informed by intersectional frameworks. Inequities related to class, race, and gender highlight the importance of disaggregated data collection as standard practice. Targeted interventions, such as workplace protections for racialized women in precarious jobs, are critical to addressing compounded vulnerabilities and ensuring equity in pandemic responses.
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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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".