Health Care Workers in the setting of the “Arab Spring”: a scoping review for the Lancet-AUB Commission on Syria
Bibliographic record
Abstract
BACKGROUND: "Health Care Workers in Conflict Areas" emerged as one of the priority themes for a Lancet Commission addressing health in conflict. The objective of our study was to conduct a scoping review on health workers in the setting of the Syrian conflict, addressing four topics of interest: violence against health care workers, education, practicing in conflict setting, and migration. METHODS: Considering the likelihood of scarcity of data, we broadened the scope of the scoping review to include indirect evidence on health care workers from other countries affected by the "Arab Spring". We electronically searched six electronic databases. We conducted descriptive analysis of the general characteristics of the included papers. We also used the results of this scoping review to build an evidence gap map. RESULTS: Out of the 11 165 identified citations, 136 met our eligibility criteria. The majority of the articles tackled the issue of violence against health care workers (63%) followed by practicing in conflict setting (19%), migration (17%) and education (10%). Countries in focus of most articles were: Syria (35%), Iraq (33%), and Bahrain (29%). News, editorials, commentaries and opinion pieces made up 81% of all included papers, while primary studies made up only 9%. All the primary studies identified in this review were conducted on Iraq. Most of the articles about violence against health care workers were on Bahrain, followed by Syria and Iraq. The first and corresponding authors were most frequently affiliated with institutions from non-Arab countries (79% and 79% respectively). CONCLUSIONS: Research evidence on health care workers in the setting of the "Arab Spring" is scarce. This review and the gap map can inform the research agendas of funders and researchers working in the field of health care workers in conflict setting. More well-designed primary studies are needed to inform the decisions of policymakers and other interested parties.
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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 | 0.040 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".