Experiences and perceptions of migrant populations in South Africa on COVID-19 immunization: an interpretative phenomenological analysis
Bibliographic record
Abstract
INTRODUCTION: Migrant populations (asylum seekers, permit holders, refugees, and undocumented migrants) living in South Africa face various individual, social, and physical circumstances that underpin their decisions, motivation, and ability to receive the COVID-19 vaccine. We conducted a qualitative study to explore the experiences and perceptions of migrant populations in South Africa on COVID-19 vaccines to inform recommendations for improved COVID-19 immunization. METHODS: We conducted an Interpretative Phenomenological Analysis (IPA) with 20 asylum seekers, permit holders, refugees, and undocumented migrants living in South Africa. We applied a maximum variation purposive sampling approach to capture all three categories of migrants in South Africa. Semi-structured interviews were conducted and recorded electronically with consent and permission from the study participants. The recordings were transcribed and analyzed thematically following the IPA using Atlas.ti version 9. RESULTS: Four major reflective themes emanated from the data analysis. (1) While some migrants perceived being excluded from the South African national immunization program at the level of advertisement and felt discriminated against at the immunization centers, others felt included in the program at all levels. (2) Skepticism, myths, and conspiracy theories around the origin of SARS-CoV-2 and the COVID-19 vaccine are pervasive among migrant populations in South Africa. (3) There is a continuum of COVID-19 vaccine acceptance/hesitancy ranging from being vaccinated through waiting for the chance to be vaccinated to refusal. (4) Accepting the vaccine or being hesitant follows the beliefs of the participant, knowledge of the vaccine's benefits, and lessons learned from others already vaccinated. CONCLUSION: COVID-19 vaccine inclusiveness, awareness, and uptake should be enhanced through migrant-aware policies and actions such as community mobilization, healthcare professional training, and mass media campaigns.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".