Health-related articles on Syria before and after the start of armed conflict: a scoping review for The Lancet-American University of Beirut Commission on Syria
Bibliographic record
Abstract
INTRODUCTION: Armed conflict may influence the size and scope of research in Arab countries. We aimed to assess the impact of the 2011 Syrian conflict on health articles about Syria published in indexed journals. METHODS: We conducted a scoping review on Syrian health-related articles using seven electronic databases. We included clinical, biomedical, public health, or health system topics published between 1991 and 2017. We excluded animal studies and studies conducted on Syrian refugees. We used descriptive and social network analyses to assess the differences in rates, types, topics of articles, and authorship before and after 2011, the start of the Syrian conflict. RESULTS: Of 1138 articles, 826 (72.6%) were published after 2011. Articles published after 2011 were less likely to be primary research; had a greater proportion reporting on mental health (4.6% vs. 10.0%), accidents and injuries (2.3% vs. 18.8%), and conflict and health (1.7% vs. 7.8%) (all p < 0.05); and a lower proportion reporting on child and maternal health (8.1 to 3.6%, p = 0.019). The proportion of research articles reporting no funding increased from 1.1 to 14.6% (p < 0.01). While international collaborations increased over time, the number of articles with no authors affiliated to Syrian institutions overtook those with at least one author affiliation to a Syrian institution for the first time in 2015. CONCLUSION: To our knowledge, this is the first study to examine the impact of armed conflict on health scholarship in Syria. The Syrian conflict was associated with a change in the rates, types, and topics of the health-related articles, and authors' affiliations. Our findings have implications for the prioritization of research funding, development of inclusive research collaborations, and promoting the ethics of conducting research in complex humanitarian settings.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".