Terrorist attack of 15 January 2016 in Ouagadougou: how resilient was Burkina Faso's health system?
Bibliographic record
Abstract
In Africa, health systems are often not very responsive. Their resilience is often tested by health or geopolitical crises. The Ebola epidemic, for instance, exposed the fragility of health systems, and recent terrorist attacks have required countries to respond to urgent situations. Up until 2014, Burkina Faso's health system strongly resisted these pressures and reforms had always been minor. However, since late 2014, Burkina Faso has had to contend with several unprecedented crises. In October 2014, there was a popular insurrection. Then, in September 2015, the Security Regiment of the deposed president attempted a coup d'état. Finally, on 15 January 2016, a terrorist attack occurred in the capital, Ouagadougou. These events involved significant human injury and casualties. In these crises, the Burkinabè health system was sorely tried, testing its responsiveness, resiliency and adaptability. We describe the management of the recent terrorist attack from the standpoint of health system resilience. It would appear that the multiple crises that had occurred within the previous 2 years led to appropriate management of that terrorist attack thanks to the rapid mobilisation of personnel and good communication between centres. For example, the health system had put in place a committee and an emergency response plan, adapted blood bank services and psychology services, and made healthcare free for victims. Nevertheless, the system encountered several challenges, including the development of framework documents for resources (financial, material and human) and their use and coordination in crisis situations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".