Monitoring Attacks on Health Care as a Basis to Facilitate Accountability for Human Rights Violations
Bibliographic record
Abstract
Violence against health care systems is an assault on health and human rights. Despite the evolution of global standards to protect health workers and ensure the delivery of health care in times of conflict, attacks against health systems have continued throughout the world—violating humanitarian law, undermining human rights, and threatening public health. The persistence of such violence against health care, especially in humanitarian crises related to armed conflict, has prompted global institutions to develop systematic monitoring mechanisms in an effort to alleviate these harms, seeking to protect health workers from being harmed for their healing efforts. This article examines the development and implementation of the World Health Organization (WHO) Surveillance System of Attacks on Healthcare (SSA) as a systematic mechanism to collect and disseminate data concerning attacks on health care systems. Although the SSA provides a foundation for monitoring attacks in conflict zones, this research considers whether the SSA has collected the necessary data, categorized these data appropriately, and disseminated sufficient information to facilitate human rights accountability, analyzing the political, methodological, and institutional challenges faced by WHO. The article concludes that refinements to this monitoring mechanism are needed to strengthen the political prioritization, research methodology, and institutional implementation necessary to ensure accountability for violations of health and human rights.
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 | 0.133 | 0.206 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".