Equitable access to COVID-19 vaccines in Botswana: a scoping review
Bibliographic record
Abstract
Introduction: Despite global market complexities, Botswana acquired about 2.6 million COVID-19 vaccine doses between March 2021 and March 2022, 76% of which were purchased while 24% were donations. Thus, the study was envisaged to aggregate evidence on the case of Botswana's COVID-19 vaccine access patterns, hesitancy, and uptake. Materials and methods: We conducted a scoping reviewof Botswana-based articles using a predetermined search strategy to search databases including Medline, CINAHL, Web of Science, PubMed, Scopus, and Google Scholar. The review included all the English-language written peer-reviewed and grey literature reporting on vaccination in Botswana, to broaden coverage in recognition of limited publications on COVID-19 vaccinartion in Botswana. Non-English articles were excluded due to limited translation resources. Due to the heterogeneity of studies, a narrative synthesis approach was used to collect, synthesize, and map the literature. Results: As of 31 December 2021, 80.6% of the Botswana national target of 1,390,856 people over 18 years had received at least one dose of a COVID-19 vaccine, while 71.9% were fully vaccinated. Various vaccine distribution channels were utilized, including public facilities and outreaches, to improve access and uptake of vaccines. COVID-19 vaccine acceptance was considered generally high (73.4% amongst adults), and found positively associated with the male gender, those with comorbidities, those with non-restrictive religious beliefs, and those aged 55-64 years who thought the vaccine was safe for use. COVID-19 vaccine delivery relied on existing Expanded Program on Immunization (EPI) structures and therefore experienced to existing EPI challenges including, lack of transport, shortage of human resources, and vaccine stock-outs. Conclusions: Under-performance of immunization programs at the district level, characterized by declining immunization coverage and inadequate outreach services, exacerbates disparities in vaccine access. Efforts to strengthen healthcare infrastructure and expand outreach services are essential for reaching populations with limited access to healthcare facilities, particularly in rural and hard-to-reach areas. Collaboration with other government entities and the private sector improved vaccine access.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".