An evaluation of emergency guidelines issued by the World Health Organization in response to four infectious disease outbreaks
Bibliographic record
Abstract
BACKGROUND: The production of high-quality guidelines in response to public health emergencies poses challenges for the World Health Organization (WHO). The urgent need for guidance and the paucity of structured scientific data on emerging diseases hinder the formulation of evidence-informed recommendations using standard methods and procedures. OBJECTIVES: In the context of the response to recent public health emergencies, this project aimed to describe the information products produced by WHO and assess the quality and trustworthiness of a subset of these products classified as guidelines. METHODS: We selected four recent infectious disease emergencies: outbreaks of avian influenza A-H1N1 virus (2009) and H7N9 virus (2013), Middle East respiratory syndrome coronavirus (MERS-CoV) (2013), and Ebola virus disease (EVD) (2014 to 2016). We analyzed the development and publication processes and evaluated the quality of emergency guidelines using AGREE-II. RESULTS: We included 175 information products of which 87 were guidelines. These products demonstrated variable adherence to WHO publication requirements including the listing of external contributors, management of declarations of interest, and entry into WHO's public database of publications. For guidelines, the methods for development were incompletely reported; WHO's quality assurance process was rarely used; systematic or other evidence reviews were infrequently referenced; external peer review was not performed; and they scored poorly with AGREE II, particularly for rigour of development and editorial independence. CONCLUSIONS: Our study suggests that WHO guidelines produced in the context of a public health emergency can be improved upon, helping to assure the trustworthiness and utility of WHO information products in future emergencies.
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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 | 0.333 | 0.693 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.032 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".