Human Rights Violations and Mistrust among Refugees in South Africa: Implications for Public Health during the COVID Pandemic
Bibliographic record
Abstract
Despite the open policy of integration, refugees in South Africa have been experiencing increasing exclusion and discrimination in socio-economic development and from social services. State-sanctioned discrimination contributes to mistrust among marginalized groups toward the government and its institutions. However, public trust towards healthcare authorities and government institutions is critical during pandemic outbreaks to ensure the population’s willingness to follow public health initiatives and protocols to contain the spread of a pandemic. Eleven key informants, including refugee community leaders and refugee-serving NGOs, were virtually interviewed about refugees’ access to healthcare in South Africa during the COVID-19 pandemic and the consequences of inconsistent access and discrimination on their trust of public healthcare initiatives. Interviews were analyzed using critical thematic analysis. The results suggest that refugees’ access to public healthcare services were perceived as exclusionary and discriminatory. Furthermore, the growing mistrust in institutions and authorities, particularly the healthcare system, and misperceptions of COVID-19 compromised refugees’ trust and adherence to public health initiatives. This ultimately exacerbates the vulnerability of the refugee community, as well as the wellbeing of the overall population.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| 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".