Supporting the pandemic response and timely access to COVID-19 vaccines: a case for stronger priority setting and health system governance in Nigeria
Bibliographic record
Abstract
Priority setting and health system governance are critical for optimising healthcare interventions and determining how best to allocate limited resources. The COVID-19 pandemic has buttressed the need for these especially now that vaccines are available to curb the spread of the disease. In many low- and middle-income countries (LMICs), vaccine coverage remains low, due in large part to sub-optimal priority setting and health system governance which has led to inequities in access and has fuelled vaccine hesitancy. An analysis of the situation in Nigeria identified key issues that have affected the health system response to COVID-19 and impeded timely access to the vaccine. These include weak vaccine procurement strategies, limited evidence on strategies for prioritising recipients and approaches for rolling out mass vaccination programmes for the entire population, lack of a communication strategy to reduce the incidence of vaccine hesitancy and failures to proactively address vaccine hesitancy through the implementation of vaccination programmes. Nigeria and other many other LMICs are still facing the prospect of subsequent and potentially worsening waves of the COVID-19 pandemic. Without effective priority setting, there is a risk that the country will not accelerate vaccine rollout quickly enough to achieve high coverage rates that will ensure herd immunity. In the context of existing weaknesses in health system governance, there is an urgent need to strengthen priority settings in Nigeria and identify and implement context-specific solutions that can improve vaccine coverage for the population.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".