Assessing trade in health services in countries of the Eastern Mediterranean from a public health perspective
Bibliographic record
Abstract
SUMMARY: Assessing trade in health services (TiHS) in developing countries is challenging since the sources of information are diverse, information is not accessible and professionals lack grasp of issues. A multi-country study was conducted in the Eastern Mediterranean Region (EMR)--Egypt, Jordan, Lebanon, Morocco, Oman, Pakistan, Sudan, Syrian Arab Republic, Tunisia, and Yemen. The objective was to estimate the direction, volume, and value of TiHS; analyze country commitments; and assess the challenges and opportunities for health services.Trade liberalization favored an open trade regime and encouraged foreign direct investment. Consumption abroad and movement of natural persons were the two prevalent modes. Yemen and Sudan are net importers, while Jordan promotes health tourism. In 2002, Yemenis spent US$ 80 million out of pocket for treatment abroad, while Jordan generated US$ 620 million. Egypt, Pakistan, Sudan and Tunisia export health workers, while Oman relies on import and 40% of its workforce is non-Omani. There is a general lack of coherence between Ministries of Trade and Health in formulating policies on TiHS.This is the first organized attempt to look at TiHS in the EMR. The systematic approach has helped create greater awareness, and a move towards better policy coherence in the area of trade in health services.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".