WHO Infection Prevention and Control Health Facility Rapid Assessment Tool for Ebola and Marburg Diseases: a validation mixed-method study
Bibliographic record
Abstract
Abstract Introduction/Objectives: The global population continually faces pathogenic threats from multiple outbreaks annually. These outbreaks may challenge healthcare facilities’ (HF) capacity to provide care while maintaining patients’ and health workers’ safety. Effective infection prevention and control measures are the cornerstone to mitigating and controlling public health events. This study validates the World Health Organization (WHO) IPC health facility rapid assessment tool (RAT) designed for evaluating health facility IPC preparedness and response during Orthoebolavirus disease (EBOD) or Orthomarburgvirus disease (MARD) outbreaks. Methods: This operational research study used a mixed method, combining a prospective evaluation of inter-rater reliability assessment of HF and FGD of assessors. Three assessors applied RAT independently in 51 health facilities in Uganda during the 2022 Sudan virus outbreak. Results: The tool proved feasible to administer, with a median completion time of 62 minutes per facility. The IPC RAT exhibited good internal consistency and inter-rater reliability, with an average Fleiss’ Kappa of 0.4 across the 15 components, suggesting a moderate-to-high consistency among assessors. Recommendations for improving the tool included revising some items for clarity and relevance, adding items to cover additional IPC aspects, developing a user guide and training materials, enhancing the scoring system, data visualization, and analysis dashboard. Conclusion: The IPC EBOD-RAT is a reliable tool for rapidly assessing IPC in HF, during EBOD/MARD outbreaks. This study highlights the need to further refine the tool in various settings and contexts, and to develop user guidance and training materials.
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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 | 0.106 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".