Adverse Impacts of Imposing International Economic Sanctions on Health.
Bibliographic record
Abstract
BACKGROUND: International economic sanctions (IES) influence a country's economic development and the overall welfare of a nation's population. METHODS: An electronic search of PubMed, Embase and Web of Science was conducted until July 31, 2019. Additionally, a list of references to related articles was reviewed. Key search terms were "Economics", "Health", "Sanction", and their equivalents with no language or time restriction. RESULTS: Totally, 8624 records were identified of which 2869 duplicates were deleted. Finally, 24 papers met the inclusion criteria and were selected for drafting. The number of papers included for evaluating each factor included healthcare (n=16) and pharmaceutical industry (n=10). Nine and eight studies examined the effect of sanctions imposed on Iran and Iraq, respectively. France, Haiti, Serbia, Cuba, Syria, and other areas such as Africa were also evaluated. Sanctions lead to a decrease in immunization rates and government health care expenditures. Sanctions increase infant and under-five mortality rate, road traffic injuries and fatalities, severe malnutrition, infective diseases, neurologic and visual disorders, as well as shortage of medical or dental instruments and a variety of medicines. Sanctions have adverse impacts on female labor and are associated with disabling hospitals, dispersing medical workers, and facilities for radiation therapy. CONCLUSION: The health status of sanctioned nations in terms of healthcare, and pharmaceutical industry was adversely affected in targeted countries.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".