The Violence of Non-Violence: A Systematic Mixed-Studies Review on the Health Effects of Sanctions
Bibliographic record
Abstract
The use of sanctions as a policy tool to affect change in the political behavior of target states has increased over the past 30 years, along with a concern about their impact on civilian health. Some researchers have proposed that targeting sanctions can avoid their moral costs, yet others have challenged this claim. This systematic mixed-studies review explored the debate about targeted sanctions by appraising their health effects as reported in the medical and public health literature, with a global focus and through the COVID-19 era.We searched three electronic databases without temporal or geographical restrictions and identified 50 studies spanning three decades (1992–2021) meeting our inclusion criteria. Using a piloted form, we extracted quotations addressing our research questions and identified themes that we grouped according to the effects of sanctions on health or its determinants, generating frequency distributions to assess the strength of support for each theme. While no study posited a causal relationship between sanctions and health, or engaged the morality of sanctions, most implied that when sanctions were present, health was inevitably impacted, even for sanctions ostensibly targeted to minimize civilian harm. Our findings suggest that given the integrated nature of the global economy, it is all but impossible to design sanctions that will achieve their stated goals without inflicting significant harm on civilians. We conclude that the use of sanctions as a policy tool threatens global health and human rights, especially in times of crises.
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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 | 0.026 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".