Adverse Impacts of Imposing International Economic Sanctions on Health.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it