Examining the roles of partnerships in enhancing the health systems response to COVID-19 in Nigeria
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic overwhelmed the health systems and socio-economic foundations of many countries, Nigeria inclusive. The study was carried out to assess, understand, document and report the activities/measures that are considered nationally and sub-nationally significant, both in terms of COVID-19 responses and in terms of strengthening the health system for the future, in response to future threats since this will not be the last pandemic This paper examines how partnerships contributed to the health system and other sectors' responses to COVID - 19 infection in Nigeria. METHODS: This was a qualitative study. Data was collected using a scoping literature review and key informant interviews with 36 key stakeholders in the COVID-19 response in Nigeria, in Abuja (national level) Lagos and Enugu states (sub-national level). Interviews were recorded and transcribed verbatim. The qualitative data was analysed using thematic analysis. RESULTS: It was found that many partnerships were formed when responding to the COVID-19 pandemic in Nigeria. The health system leaned towards a horizontal dimension of partnership with non-health governmental sectors, non-governmental sectors, and other countries. All the components of the health system building blocks had a measure of partnership contributing to its accomplishments The partnerships came in varied forms, ranging from advocacy, funding, provision of palliatives to the citizens because of lockdowns, technical assistance, support to research, development of guidelines and health educational materials. CONCLUSION: The health sector's collaboration with other sectors strengthened all the building blocks of the health system and was invaluable in enhancing the response to COVID-19, which needed a whole of government and a multi-sectoral approach. Formal frameworks for quickly initiating whole-of-government and multi-sectoral partnerships should be developed, with clear roles and responsibilities. This should be deployed for health system resilience and for response to shocks such as the COVID-19 pandemic.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".