P161 Adopting a political economy approach to HIV research: a case study of ongoing conflict in ukraine
Bibliographic record
Abstract
Background Armed conflict erupted in eastern Ukraine in 2014. Ukraine has the highest HIV rates in Europe, there is concern that the epidemic can worsen in the current climate. Past research on HIV prevalence in conflict zones has been limited and the few studies that exist yield contradictory results. In this paper we describe the historical events leading up to the current conflict and explore its politico-socio-economic consequences as related to HIV risk. Methods This project takes a political economy approach to examine Ukraine as a case study to understand the impact of conflict on HIV and HCV. We undertook archival research to examine the structural factors related to the current conflict and its politico-socio-economic consequences. Political economy draws upon economic, political, historical, cultural and sociological approaches to examine the evolution of states, markets and society. This perspective accounts for a wide range of factors that influence the downstream realities of people living with HIV. It illuminates the structural parameters of conflict within which the epidemics exists. Results Preliminary results reveal that the social, political, and economic turmoil leading up to the armed conflict can be traced back to Ukraine’s formation as a sovereign state following the dissolution of the Soviet Union. These factors have also been associated with the beginning of Ukraine’s HIV epidemic. High inflation, deep recessions, and a bourgeoning kleoptocracy led to civil unrest and the ousting of the president which was followed by backlash from Russia. The ensuing conflict has ignited several factors known to contribute to HIV risk such as violence, migration and increased mobilization of armed forces might be expected to exacerbate prevalence. Conclusion Ukraine as a case study presents a unique opportunity to examine the influences of conflict on the HIV epidemic before, during and possibly post conflict. Disclosure No significant relationships.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".