Impact of infection prevention and control training on health facilities during the Ebola virus disease outbreak in Guinea
Bibliographic record
Abstract
BACKGROUND: In 2014-2016, West Africa faced the most deadly Ebola Virus Disease (EVD) outbreak in history. A key strategy to overcome this outbreak was continual staff training in Infection Prevention and Control (IPC), with a focus on Ebola. This research aimed to evaluate the impact of IPC training and the quality of IPC performance in health care facilities of one municipality of Conakry, Guinea. METHODS: This study was conducted in February 2016. All health facilities within Ratoma municipality, Conakry, Guinea, were evaluated based on IPC performance standards developed by the Guinean Ministry of Health. The IPC performance of healthcare facilities was categorised into high or low IPC scores based on the median IPC score of the sample. The Mantel-Haenzsel method and logistic regression were used for statistical analysis. RESULTS: Twenty-five percent of health centres had one IPC-trained worker, 53% had at least two IPC-trained workers, and 22% of health centres had no IPC-trained workers. An IPC score above median was positively associated with the number of trained staff; health centres with two or more IPC-trained workers were eight times as likely to have an IPC score above median, while those with one IPC-trained worker were four times as likely, compared to centres with no trained workers. Health centres that implemented IPC cascade training to untrained medical staff were five times as likely to have an IPC score above median. CONCLUSIONS: This research highlights the importance of training healthcare staff in IPC and organising regular cascade trainings. IPC strategies implemented during the outbreak should continue to be reinforced for the better health of patients and medical staff, and be considered a key factor in any outbreak response.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".