Identifying priority challenges and solutions for COVID-19 vaccine delivery in low- and middle-income countries: A modified Delphi study
Bibliographic record
Abstract
BACKGROUND: The rapid implementation of global COVID-19 vaccination programs has surfaced many challenges and inequities, particularly in low- and middle-income countries (LMICs). However, there continues to be a lack of consensus on which challenges are global priorities for action, and how to best respond to them. This study uses consensus-based methods to identify and rank the most important challenges and solutions for implementation of COVID-19 vaccination programs in LMICs. METHODS: We conducted a three-round modified Delphi study with a global panel of vaccine delivery experts. In Round I, panelists identified broad topical challenges and solutions. Responses were collated and coded into distinct items. Through two further rounds of structured, iterative surveys panelists reviewed and ranked the identified items. Responses were analyzed qualitatively and quantitatively to achieve consensus on the most important COVID-19 vaccine delivery challenges and solutions. RESULTS: Of the 426 invited panelists, 96 completed Round I, 56 completed Round II, and 39 completed Round III. Across all three rounds there was equal representation by gender, and panelists reported work experience in all World Bank regions and across a variety of content areas and organizations. Of the 64 initially identified items, the panel achieved consensus on three challenges and 10 solutions. Challenges fell under themes of structural factors and infrastructure and human and material resources, while solutions also included items within themes of communication, community engagement, and access and planning, processes, and operations. CONCLUSION: COVID-19 vaccine delivery is challenged by long-standing and structural inequities that disadvantage health service delivery in LMICs. These findings can, and should, be used by global health organizations to efficiently and optimally direct resources to respond to these key challenges and solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".