Global inequity creates local insufficiency: A qualitative study of COVID-19 vaccine implementation challenges in low-and-middle-income countries
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has amplified pre-existing challenges to health promotion and care across the world, and particularly in low- and middle-income countries (LMICs). This qualitative study draws on data from a panel of immunisation experts and uses a novel framework of vaccine delivery domains to explore perspectives from those who live and work in these settings on the challenges to implementing COVID-19 vaccine programs in LMICs. METHODS: We conducted a thematic content analysis of 96 participant free text replies to questions from Round I of a three-round Delphi consensus study amongst global experts on COVID-19 vaccine implementation. RESULTS: Participant responses highlighted challenges to vaccine program implementation including issues related to equity; governance, decision-making, and financing; regulatory structures, planning, and coordination; prioritisation, demand generation, and communication; vaccine, cold chain, logistics, and infrastructure; service delivery, human resources, and supplies; and surveillance, monitoring, and evaluation. CONCLUSION: We reflect on our findings in light of global efforts to address vaccine inequity and emphasise three key areas salient to improving vaccination efforts during novel infectious disease outbreaks: 1) Ensuring safe and sustainable service delivery in communities and at points of care; 2) Strengthening systems for end-to-end delivery of vaccines, therapeutics, diagnostics, and essential supplies; 3) Transforming structural paradigms towards vaccine equity.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".