How did support systems in Western countries transform and adapt to meet underserviced and marginalized migrants' needs? A scoping review
Bibliographic record
Abstract
Prior studies on the impacts of the COVID-19 pandemic on migrants with precarious immigration status (refugees, asylum seekers, and undocumented migrants who arrived in a host country fewer than five years ago) have shown that they have been disproportionately affected by the pandemic because of their migration and socioeconomic status. Across the world, support systems for these marginalized migrants at local levels had to be reinvented to face the pandemic and ensure continuity of services. The objective of this systematic review was to provide a portrait of the interventions that were set up to address underserviced and marginalized migrants’ needs in Western countries during the pandemic and to categorize them by area of intervention. We identified peer-reviewed papers published in English and French between March 2020 and February 2022 in MEDLINE, Embase, PubMed and Web of Science. We included original research studies, commentaries, essays and editorials. For grey literature, we searched in Google Scholar and the websites of major health organizations and institutions that worked with underserviced and marginalized migrants. We also consulted the articles’ list of references. We included 31 publications: 15 peer-reviewed articles and 16 grey literature documents. Our results indicated that in order to address vulnerable newcomers’ needs, support systems intervened in the following areas: immigration, health and social services; raising awareness about COVID-19, food security, primary and secondary basic needs; and mental health and control of COVID-19 infection in settings with high concentrations of underserviced and marginalized migrants. Selected interventions adopted a collaborative approach between actors in different sectors. Most of the interventions were carried out by community-based organizations. Our scoping review highlights the role of community-based organizations in improving the living conditions of migrants with precarious immigration status during the pandemic and emphasizes the relevance of intersectoral collaboration as a strategy to respond effectively to the needs of underserviced and marginalized migrants in times of crisis.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".