Sharpening our public health lens: advancing im/migrant health equity during COVID-19 and beyond
Bibliographic record
Abstract
BACKGROUND: Differential impacts of the COVID-19 pandemic have brought deeply rooted inequities to the forefront, where increasing evidence has shown that racialized immigrant and migrant (im/migrant) populations face a disproportionate burden of COVID-19. Im/migrant communities may be worst affected by lockdowns and restrictive measures, face less opportunity to physically distance or stay home sick within 'essential' jobs, and experience severe barriers to healthcare. Insufficient attention to experiences of racialized im/migrants in current pandemic responses globally highlights an urgent need to more fulsomely address unmet health needs through an anti-racist, equity-oriented lens. This commentary aims to highlight the need for public health and clinical training, research, and policy to thoughtfully prioritize im/migrant health equity during and beyond the COVID-19 pandemic. MAIN TEXT: Global pandemic responses have neglected im/migrants by continuing to ignore or insufficiently address inequities, exacerbating COVID transmission, xenophobia, and occupational injustice. Deaths, illness, stress, and other negative outcomes of the overlapping epidemics of COVID-19 and structural racism disproportionately borne by racialized im/migrants suggest the urgent need for action. As evidence mounts about how im/migrants have been left behind in times of crises, we need enhanced focus on health equity within COVID-19 research and interventions, including research that examines and pursues structural interventions necessary to mitigate these impacts, and that identifies patterns and harms of xenophobic policy, structural racism, and white supremacy in shaping im/migrant health outcomes. We must also strengthen anti-racist and equity-oriented curriculum within health education, and ensure sufficient attention to the needs of im/migrant communities within public health, clinical, and research training. CONCLUSION: The COVID-19 pandemic has exacerbated and rendered more visible the deeply rooted health and social inequities faced by racialized im/migrants across diverse settings. We argue for a greater emphasis on equity-focused and anti-racist im/migrant health research, interventions, and training. Policymakers and practitioners must ensure that healthcare policies and practices do not exacerbate inequities, and instead meaningfully address unmet needs of communities, including racialized im/migrants. Ethical and respectful community engagement, commitment and collaboration with global, national, and local communities, policymakers, academics, and educators, as well as accountability across sectors, is critical.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| 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".