Community-based Case Studies of Vaccine Hesitancy and the COVID-19 Response in South Africa; The VaxScenes Study
Bibliographic record
Abstract
Abstract Background South Africa has reported more than half of all COVID-19 cases and deaths in Africa. The South African government has launched a large COVID-19 immunization campaign with the goal of reaching more than 40 million individuals. Nonetheless, certain international, largely internet-based surveys have shown a significant proportion of vaccine hesitancy in South Africa. This study aims to determine and co-create with local stakeholders a comprehensive understanding of vaccine hesitancy and opportunities to support the promotion of other COVID-19 health-promoting behaviours at community level. Methods and design A mixed-methods, multiple case-study design; informed by the socio-ecological model of behaviour change. Four socio-economically diverse communities across South Africa will be selected and data collection will take place concurrently through three iterative phases. Phase 1 will provide insights into community experiences of COVID-19 (response) through desktop mapping exercises, observations, in-depth interviews, and focus group discussions (FGDs) designed as expression sessions with local stakeholders. Phase 2 will explore the extent and drivers of community acceptance of COVID-19 vaccines. This phase will comprise a quantitative survey based on WHO’s Behavioural and Social Drivers of Vaccination tool as well as further FGDs with community members. Phase 3 will involve cross-case study syntheses and presentation of findings to national role-players. Discussion This study will provide ground up, locally responsive, and timeous evidence on the factors influencing COVID-19 health-seeking behaviours to inform ongoing management and mitigation of COVID-19 in South Africa. It will also provide insights into the applicability of a novel vaccine hesitancy model in Africa.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".