Healthcare accessibility in yemen’s conflict zones: comprehensive review focused on strategies and solutions
Bibliographic record
Abstract
The nine-year ongoing conflict in Yemen accumulated humanitarian crisis, and severely struggling healthcare system. In the current review, we are trying here to elucidate the many perspective areas where the conflict in Yemen has made it harder to access medical care, emphasizing how the war has negatively affected medical infrastructure, caused a severe shortage of medical supplies, and obstructed access to or the ability to receive medical services. We conducted a comprehensive search across reports from in-ground working organizations like UN, MSF, ICRC, and official authorial channels, including local organizations as well to illustrate how the conflict-induced challenges have drastically limited access to essential services, as well as literature repositories (PubMed MEDLINE, Scopus, Web of Science). Then, data were thematically presented.Our data suggest urgent and thoughtful long-term solutions, including the need of economic support, reconstructing healthcare infrastructure through coordinated efforts, and setting up safe supply lines to ensure a steady flow of medical resources particularly in intensive war zones where mobile clinics could serve as an alternative. Additionally, we highlight the importance of supporting and incentivizing the healthcare workforce to prevent further depletion through training programs that include professional and practical skills and ensuring safe transport to and from medical facilities for both patients and healthcare personnel. Moreover, we recommend implementing targeted programs to improve access to quality healthcare for disproportionately affected populations, guaranteeing access to medical treatment as a right and not a privilege, and most significantly, ensuring that the medical facilities are not targeted. Therefore, these focused recommendations aim to guide policymakers, international donors, and on-ground NGOs in restoring healthcare access and improving the quality of life for millions of Yemenis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".