Upstream factors impacting COVID-19 vaccination rates across Africa: A systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Upstream factors have been found to affect COVID-19 vaccination rates and coverage globally. However, there are inadequate details within the African context. This systematic review aims to close this research gap by investigating upstream factors influencing COVID-19 vaccination rates in Africa. METHODS: A literature search will be systematically conducted utilizing various databases including: MEDLINE, EMBASE, SCOPUS, CINAHL, Web of Science, and PsycINFO. Eligible studies will include peer-reviewed articles published in the English language from 2020-2023, conducted in Africa, focused on upstream factors, and include one barrier or facilitator to COVID-19 vaccination rates. Two reviewers will use a two-step screening process to examine every article's title, abstract, and full text. A third-party reviewer will resolve disagreements between both individual reviewers. This review will focus on extracting data from published studies to explain the upstream factors included and their impact on COVID-19 vaccination rates across Africa. Data and records will be managed using Covidence. Preferred Reporting Items for Systematic Reviews and Meta-Analyses [PRISMA] framework will be used as the basis for reporting. To reduce bias, the researchers will use the Mixed Methods Appraisal Tool to assess the studies chosen for review. Results will be compiled utilizing four tables to summarize articles and group determinants based on the Consolidated Framework for Implementation Research (CFIR). DISCUSSION: Upstream factors have been cited as affecting population health, vaccination programs, and COVID-19, yet a large-scale systematic review has not been conducted to investigate these factors in relation to COVID-19 vaccination disparities faced in Africa. This review aims to analyze the root causes of African vaccination disparities by focusing on upstream factors. Understanding these factors is vital to help explain why these disparities occur and for designing effective interventions for future vaccinations. The results are expected to provide insights for researchers, policymakers, health systems, and individuals by identifying how resources and efforts can be better utilized to improve vaccination uptake and access. TRIAL REGISTRATION: Systematic review registration: CRD42024501293.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.113 | 0.096 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.016 | 0.014 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.072 | 0.014 |
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".