Show solidarity with the Congolese people in the 10th Ebola outbreak declared a health emergency of international concern: understand a qualitative study of variables of hospital activities on infection control practices in Kinshasa city
Bibliographic record
Abstract
Abstract Health workers play an important role during epidemics, but there is limited research on hospital activities on infection control practices in the Democratic Republic of the Congo and how health workers can cope during a probable health epidemic in Kinshasa city. The determinants of the current Ebola Virus Disease in the geographical distribution remain poorly understood. The World Health Organization’s Health Regulation Committee decided on Wednesday July 17 th , 2019 to declare the Ebola haemorrhagic fever epidemic in the provinces of North Kivu and Ituri as a health emergency of international concern. The country struggles to control it against a backdrop of a health system that is already overburdened. To test the influence of the challenges of a contamination in the context of an Ebola outbreak that may face health workers and their coping strategies in thirteen hospitals of reference in Kinshasa, we conducted a survey hoping to educate or remember good practices for health workers in Kinshasa that is also available for health workers in the East Area of the country in which the ongoing Ebola outbreak progress is spreading (North Kivu and Ituri). For the ongoing outbreak, we obtained data from the Ministère de la Santé Publique of the Democratic Republic of the Congo where cases are classified as suspected, probable, or confirmed using national case definitions. We found that the ongoing Ebola virus outbreak in the Democratic Republic of the Congo has similar epidemiological features to previous Ebola virus disease outbreak in Sierra Leone that was well described. For the qualitative study about the biosecurity in thirteen hospitals of reference in Kinshasa, we found that the Bondeko-Ngaliema Monkole group has occupied the first rank, while the group Kintambo-King Baudouin-Ndjili-Makala occupied the other end of the scale; the other health facilities occupied an intermediate position. Among the 7 hospitals which were placed at the top of this classification of biosecurity, 5 were massively subsidized by international NGO, which explains to a great extent their performances in one hand, another hand finding its explanation in the quality of their management. It is the case of Bondeko, Monkole, Kalembe-Lembe, St Joseph and Kingasani 2. Author summary The determinants of the transmission are poorly understood, but a growing body of evidence supports an important role of the lack of prevention in the dissemination of Ebola virus. The results of our study conducted in 13 hospitals of reference in Kinshasa suggest that the biosecurity measures—which were introduced in Kinshasa hospitals policies through prevention since Ebola outbreaks—have been respected by 75% and had 25% of parameters to be improved. Biosecurity is an important concept; it seems to be a vector for the prevention of Ebola Virus Disease . In addition, the lack of biosecurity observation may have a role in the contamination of Ebola Virus Disease in local populations found in invaded areas. This study provides knowledge into the preventive measures influencing Ebola Virus Disease populations, thereby determining in perspective a study on meat consumption of animals found dead in forests that will be a risk for human infection as the Democratic Republic of the Congo has many forests.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".