Health Care Workers in Danger Zones: A Special Report on Safety and Security in a Changing Environment
Bibliographic record
Abstract
INTRODUCTION: Violence against humanitarian health care workers and facilities in situations of armed conflict is a serious humanitarian problem. Targeting health care workers and destroying or looting medical facilities directly or indirectly impacts the delivery of emergency and life-saving medical assistance, often at a time when it is most needed. PROBLEM: Attacks may be intentional or unintentional and can take a range of forms from road blockades and check points which delay or block transport, to the direct targeting of hospitals, attacks against medical personnel, suppliers, patients, and armed entry into health facilities. Lack of access to vital health care services weakens the entire health system and exacerbates existing vulnerabilities, particularly among communities of women, children, the elderly, and the disabled, or anyone else in need of urgent or chronic care. Health care workers, especially local workers, are often the target. METHODS: This report reviews the work being spearheaded by the Red Cross and Red Crescent Movement on the Health Care in Danger initiative, which aims to strengthen the protections for health care workers and facilities in armed conflicts and ensure safe access for patients. This includes a review of internal reports generated from the expert workshops on a number of topics as well as a number of public sources documenting innovative coping mechanisms adopted by National Red Cross and Red Crescent Societies. The work of other organizations is also briefly examined. This is followed by a review of security mechanisms within the humanitarian sector to ensure the safety and security of health care personnel operating in armed conflicts. RESULTS: From the existing literature, a number of gaps have been identified with current security frameworks that need to be addressed to improve the safety of health care workers and ensure the protection and access of vulnerable populations requiring assistance. A way forward for policy, research, and practice is proposed for consideration. CONCLUSION: While there is work being done to improve conditions for health care personnel and patients, there need to be concerted actions to stigmatize attacks against workers, facilities, and patients to protect the neutrality of the medical mission.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".