Using the resilience theory to understand and address migrant pandemic precarity among South African migrant populations
Bibliographic record
Abstract
INTRODUCTION: "Migrant pandemic precarity" describes the specific consequences and vulnerabilities experienced by migrants during the COVID-19 pandemic. Despite their precarity, migrants adopted some resilient behaviors. Utilizing the resilience theory, our study explored how migrants in South Africa managed to cope with heightened vulnerabilities during the COVID-19 pandemic and how these resilient behaviors can provide insights into addressing the health inequities experience by this population. METHODS: We conducted an interpretive phenomenological analysis study to understand the key challenges of migrant populations in South Africa during the COVID-19 pandemic (2019-2022) and how resourceful they became in overcoming these challenges. Using a purposive sampling approach, we conducted 20 semi-structured interviews with migrants from other African countries, including asylum seekers, refugees, permit holders, and undocumented migrants in two South African provinces. RESULTS: Three interconnected aspects of migrant pandemic precarity were revealed: financial insecurities, food insecurities, and health concerns. Social connectedness and resource provision ensured inclusivity and supported these migrant populations in navigating the difficulties posed by migrant pandemic precarity. CONCLUSIONS: The South African government should implement migrant-inclusive approaches and empower structures and programs that enhance migrants' resilience to future crises. We argued that to reduce health inequities among migrant populations in South Africa, these resilience approaches can be harnessed in three ways. (1) the South African government should create mechanisms and processes to identify and integrate migrants with critical skills into their workforce. (2) enhancing collaborations between civil society organizations, local governments, and international organizations, such as the International Organization for Migration, to address food insecurities among the migrant population. (3) enforcing their constitutional mandate to provide free basic health care services to all migrants in South Africa by removing barriers such as health care provider attitudes toward migrants' access to health care services.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".