An assessment of Nigeria’s health systems response to COVID-19
Bibliographic record
Abstract
Objectives: This study aims to understand and report on selected health system interventions considered nationally and sub-nationally of particular significance both in terms of COVID-19 responses and in strengthening the health system for the future. Design: A review of published and grey literature, including journals, news/ media and official documents, was conducted from 1st December 2019 to 31st December 2020. The reviewers read and extracted relevant data using FACTIVA in a uniform data extraction template. Responses that related to service delivery were captured. Setting: The assessment considered responses at the national and two state levels: Lagos and Enugu, representing the epicentre and a low COVID-19 burden centre. Inclusion criteria: Documents and news that mentioned COVID-19 response, particularly service delivery aspects, were included in this review. Results: The identified interventions were mostly technical support targeted at health workers: including training of about 17,000 health workers, supervising and engaging more health workers, upgrading laboratories and building new ones to improve screening and diagnosis, and motivation of health workforce with incentives. Furthermore, the influx of philanthropic contributions improved the data and information systems supply of medicines, medical products and non-pharmaceutical protective materials through local production. The presence of political will and the government's efforts in health system's response to COVID-19 facilitated these interventions. Conclusions: Interventions of state and non-state actors have strengthened the health systems to some extent. However, more needs to be done to sustain these gains and make the health system resilient to absorb unprecedented shocks. Funding: IDRC Canada Grant # 109479-001.
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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 | 0.030 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".