Health aid displacement during a decade of conflict (2011–19) in Syria: an exploratory analysis
Bibliographic record
Abstract
BACKGROUND: Syria has been in continuous conflict since 2011, resulting in more than 874,000 deaths and 13.7 million internally displaced people (IDPs) and refugees. The health and humanitarian sectors have been severely affected by the protracted, complex conflict and have relied heavily on donor aid in the last decade. This study examines the extent and implications of health aid displacement in Syria during acute humanitarian health crises from 2011 to 2019. METHODS: We conducted a trend analysis on data related to humanitarian and health aid for Syria between 2011 and 2019 from the OECD's Creditor Reporting System. We linked the data obtained for health aid displacement to four key dimensions of the Syrian conflict. The data were compared with other fragile states. We conducted a workshop in Turkey and key informants with experts, policy makers and aid practitioners involved in the humanitarian and health response in Syria between August and October 2021 to corroborate the quantitative data obtained by analysing aid repository data. RESULTS: The findings suggest that there was health aid displacement in Syria during key periods of crisis by a few key donors, such as the EU, Germany, Norway and Canada supporting responses to certain humanitarian crises. However, considering that the value of humanitarian aid is 50 times that of health aid, this displacement cannot be considered as critical. Also, there was insufficient evidence of health displacement across all donors. The results also showed that the value of health aid as a proportion of aggregate health and humanitarian aid is only 2% in Syria, compared to 22% for the combined average of fragile states, which further indicates the predominance of humanitarian aid over health aid in the Syrian crisis context. CONCLUSION: This study highlights that in very complex conflict-affected contexts such as Syria, it is difficult to suggest the use of health aid displacement as an effective tool for aid-effectiveness for donors as it does not reflect domestic needs and priorities. Yet there seems to be evidence of slight displacement for individual donors. However, we can suggest that donors vastly prefer to focus their investment in the humanitarian sector rather than the health sector in conflict-affected areas. There is an urgent need to increase donors' focus on Syria's health development aid and adopt the humanitarian-development-peace nexus to improve aid effectiveness that aligns with the increasing health needs of local communities, including IDPs, in this protracted conflict.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".