Providing care under extreme adversity: The impact of the Yemen conflict on the personal and professional lives of health workers
Bibliographic record
Abstract
The war in Yemen, described as the world's 'worst humanitarian crisis,' has seen numerous attacks against health care. While global attention to attacks on health workers has increased significantly over the past decade, gaps in research on the lived experiences of frontline staff persist. This study draws on perspectives of frontline health workers in Yemen to understand the impact of the ongoing conflict on their personal and professional lives. Forty-three facility-based health worker interviews, and 6 focus group discussions with community-based health workers and midwives were conducted in Sana'a, Aden and Taiz governorates at the peak of the Yemen conflict. Data were analysed using content analysis methods. Findings highlight the extent and range of violence confronting health workers in Yemen as well as the coping strategies they use to attenuate the impact of acute and chronic stressors resulting from conflict. We find that the complex security situation - characterized by multiple parties to the conflict, politicization of humanitarian aid and constraints in humanitarian access - was coupled with everyday stressors that prevented health workers from carrying out their work. Participants reported sporadic attacks by armed civilians, tensions with patients, and harassment at checkpoints. Working conditions were dire, and participants reported chronic suspension of salaries as well as serious shortages of essential supplies and medicines. Themes specific to coping centered around fatalism and religious motivation, resourcefulness and innovation, and sense of duty and patriotism. Our findings demonstrate that health workers experience substantial stress and face various pressures while delivering lifesaving services in Yemen. While they exhibit considerable resilience and coping, they have needs that remain largely unaddressed. Accordingly, the humanitarian community should direct more attention to responding to the mental health and psychosocial needs of health workers, while actively working to ameliorate the conditions in which they work.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| 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".