Conditions for health system resilience in the response to the COVID-19 pandemic in Mauritania
Bibliographic record
Abstract
INTRODUCTION: A country's ability to manage a crisis depends on its level of resilience. Efforts are made to clarify the concept of health system resilience, but its operationalisation remains little studied. In the present research, we described the capacity of the local healthcare system in the Islamic Republic of Mauritania, in West Africa, to cope with the COVID-19 pandemic. METHODS: We used a single case study with two health districts as units of analysis. A context analysis, a literature review and 33 semi-structured interviews were conducted. The data were analysed using a resilience conceptual framework. RESULTS: The analysis indicates a certain capacity to manage the crisis, but significant gaps and challenges remain. The management of many uncertainties is largely dependent on the quality of the alignment of decision-makers at district level with the national level. Local management of COVID-19 in the context of Mauritania's fragile healthcare system has been skewed to awareness-raising and a surveillance system. Three other elements appear to be particularly important in building a resilient healthcare system: leadership capacity, community dynamics and the existence of a learning culture. CONCLUSION: The COVID-19 pandemic has put a great deal of pressure on healthcare systems. Our study has shown the relevance of an in-depth contextual analysis to better identify the enabling environment and the capacities required to develop a certain level of resilience. The translation into practice of the skills required to build a resilient healthcare system remains to be further developed.
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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 | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".