Data Synthesis: COVID-19 Vaccine Perceptions in Africa: Social and Behavioural Science Data, March 2020-March 2021 || Synthèse de données : Perceptions de la vaccination contre la covid-19 en Afrique : données des sciences sociales et comportementales, mars 2020 - mars 2021
Bibliographic record
Abstract
Safe and effective vaccines against COVID-19 are seen as a critical path to ending the pandemic. This synthesis brings together data related to public perceptions about COVID-19 vaccines collected between March 2020 and March 2021 in 22 countries in Africa. It provides an overview of the data (primarily from cross-sectional perception surveys), identifies knowledge and research gaps and presents some limitations of translating the available evidence to inform local operational decisions. The synthesis is intended for those designing and delivering vaccination programmes and COVID-19 risk communication and community engagement (RCCE). 5 large-scale surveys are included with over 12 million respondents in 22 central, eastern, western and southern African countries (note: one major study accounts for more than 10 million participants); data from 14 peer-reviewed questionnaire surveys in 8 countries with n=9,600 participants and 15 social media monitoring, qualitative and community feedback studies. Sample sizes are provided in the first reference for each study and in Table 13 at the end of this document. The data largely predates vaccination campaigns that generally started in the first quarter of 2021. Perceptions will change and further syntheses, that represent the whole continent including North Africa, are planned. This review is part of the Social Science in Humanitarian Action Platform (SSHAP) series on COVID-19 vaccines. It was developed for SSHAP by Anthrologica. It was written by Kevin Bardosh (University of Washington), Tamara Roldan de Jong and Olivia Tulloch (Anthrologica), it was reviewed by colleagues from PERC, LSHTM, IRD, and UNICEF (see acknowledgments) and received coordination support from the RCCE Collective Service. It is the responsibility of SSHAP. Des vaccins sûrs et efficaces contre la COVID-19 sont perçus comme une voie critique pour mettre fin à la pandémie.1 Cette synthèse rassemble des données inhérentes aux perceptions du public au sujet des vaccins contre la COVID-19 recueillies entre mars 2020 et mars 2021 dans 22 pays d’Afrique. Elle fournit un aperçu des données (issues principalement d’enquêtes d’opinion transversales), identifie les lacunes en matière de connaissances et de recherche et présente certaines limites à l’application des données probantes disponibles pour éclairer les décisions opérationnelles locales. La synthèse est destinée aux personnes qui conçoivent et mettent en œuvre des programmes de vaccination, ainsi que des stratégies en matière de communication des risques et d’engagement communautaire (CREC) liées à la COVID-19
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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 | 0.093 | 0.389 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.025 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 0.007 |
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".