A qualitative examination of the distribution strategies, access, and equitable uptake of the COVID-19 vaccines in Kenya: lessons for the next pandemic
Bibliographic record
Abstract
Background: The WHO and international partners identified vaccination against the coronavirus as one of the important public health interventions in controlling Corona Virus Disease (COVID-19) infections. The COVID-19 vaccine uptake was however low and characterized by inequitable distribution and access in Kenya. This study aimed to examine in depth the causes of inequity in vaccine uptake to inform the future rollout and successful uptake of new vaccines. Methods: = 32). Data were analyzed using the framework approach to allow for the identification, examination, and interpretation of patterns or themes emerging from the interview and review data. Findings: Centralized leadership provided by the Ministry of Health (MOH) utilized the existing distribution system, leveraging the Expanded Program on Immunization (EPI); enhancement of stakeholder collaboration and communication through government agencies, civil society, and community health workers was essential for the distribution of COVID-19 vaccines. Key barriers to vaccine uptake included organization and coordination of the vaccination rollout, socio-cultural barriers, and geographic challenges, particularly in rural areas, hindering vaccine access. Communication problems, particularly misinformation, were associated with public mistrust in vaccination efforts. Conclusion: Efforts to increase vaccine coverage should target organizational, social, political, and cultural norms to enhance access and uptake of quality vaccines. This study presents important lessons for health system adaptation, re-organization and preparations for future pandemics.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".