What are the key determinants to fostering equity within coronavirus disease 2019 (COVID-19) vaccination deployment initiatives in Nigeria? A scoping review
Bibliographic record
Abstract
This review aimed to identify the barriers and facilitators to equitable coronavirus disease 2019 (COVID-19) vaccine distribution in Nigeria using the consolidated framework for implementation research (CFIR). A comprehensive search strategy was applied across five databases—Web of Science, MEDLINE, EMBASE, CAB Direct, and CINAHL. The search, conducted as part of a scoping review, yielded 2,751 citations. Seven studies met the inclusion criteria after screening. Data were extracted and analyzed using CFIR constructs to identify key barriers and facilitators to equitable vaccine distribution. Six barriers were identified: limited physical and socioeconomic access, bribery, nepotism, and insufficient availability of translated information. Facilitators included community involvement as local monitoring agents, unannounced staff inspections, healthcare worker training tailored to community needs, and localized outreach strategies such as jingles and call-in programs. CFIR constructs, including Local Conditions, Tailoring Strategies, Available Resources, and Physical Infrastructure, provided a framework for analyzing the findings. This review highlights significant barriers and promising facilitators to equitable vaccine distribution in Nigeria. Targeted interventions, such as community engagement, anti-corruption measures, and culturally tailored strategies, are critical to addressing these challenges and improving access. These findings underscore the need for localized, equity-focused approaches to enhance vaccine distribution systems in Nigeria and other low-resource settings.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".