An analysis of WHO’s Temporary Recommendations on international travel and trade measures during Public Health Emergencies of International Concern
Bibliographic record
Abstract
During Public Health Emergencies of International Concern (PHEICs), The International Health Regulations (IHR) require the WHO to issue Temporary Recommendations on the use of international travel and trade measures. During the COVID-19 pandemic, WHO's initial recommendation against 'any travel or trade restriction' has been questioned, and virtually all countries subsequently used international travel measures. WHO's Recommendations to States Parties also changed over the course of the pandemic. There is a need to understand how WHO's treatment of this issue compared with other PHEICs and why States Parties' actions diverged from WHO's initial Recommendations. This first analysis of WHO's Temporary Recommendations on international travel and trade measures during all seven PHEICs compares the guidance for clarity and consistency in several areas of substance and process. We find that lack of clarity and inconsistency in WHO guidance makes it difficult to interpret and relate back to IHR obligations. Based on this analysis, we offer recommendations to increase consistency and clarity of WHO's guidance on this issue during global health 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 | 0.109 | 0.233 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".