Impact of COVID-19 on displaced populations and migrants around the world
Bibliographic record
Abstract
My practicum placement was completed with the Dalla Lana School of Public Health Centre for Global Health. I have contributed to the work of a team of student and faculty members developing a review of the literature and environmental scan to explore the impact of the COVID-19 pandemic on migrant populations. I worked with colleagues to design and run a search strategy on the Medline (OVID) and Scopus bibliographic databases. The findings showed that crises including the COVID-19 pandemic act as magnifying lens and expose existing inequities within society as the impact of the pandemic is not equally felt by all population groups. Migrant populations are particularly impacted due to their intersectional identities that marginalize and disempower them and severely impact their health outcomes. Even though migration is the engine of the globalized economy and migrant workers make significant contribution to agricultural and economic prosperity, their precarious living conditions have worsened during the pandemic and they are being excluded from relief packages and income support. Furthermore, racism and xenophobia are fuelling hostility and prejudice towards migrants as governments are controlling the movement of migrants by closing their borders to asylum seekers and existing refugee camps are having outbreaks due to cramped and overcrowded living conditions and limited healthcare access. It is evident that migrant populations are very diverse groups that are facing unique challenges and thus, require distinct forms of protection particularly during this pandemic. The results of this work are currently being summarized in a manuscript that recognizes how determinants of health impact the health and well-being of migrants, the need to develop a road map for recovery using a health equity lens, and inform health policies. To eradicate COVID-19, it is imperative to leave no one behind including migrant populations and re-evaluate how inequities are addressed globally.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".