Analysis of the availability, effectiveness and equity of deployment of resources in the health system response to COVID-19 in Nigeria
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) exposed weaknesses in the health systems of countries such as Nigeria, which affected the effectiveness of the health system response to the pandemic. This paper provides new knowledge on the level of the availability, effectiveness and equity of resources in response to COVID-19 in Nigeria. This is valuable information for improving the delivery of countermeasures against future pandemics. METHODS: The study was conducted at the federal level and in two states in Nigeria. The states were Lagos in the southwest and Enugu in the southeast. In-depth interviews were undertaken with 34 key informants. NVivo version 12 software was used for coding and thematic analysis. RESULTS: There were inadequate, inequitable and suboptimal resources (human, financial, equipment and materials) for the response. In some of the countermeasures, only people that were employed in the formal sector benefitted from the distribution of welfare materials and financial packages; the informal sector, which constitutes the majority of the poor population in Nigeria, was excluded. CONCLUSIONS: Inequity and suboptimal availability of resources to control COVID-19 led to reduced effectiveness of the health system response to the disease in Nigeria. Such negative factors must be mitigated in future responses to pandemics in the country.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".