Roadmap for rebuilding the health system and scenarios of crisis path in Gaza
Bibliographic record
Abstract
The horrific attacks on Gaza have had a profound impact on Gaza's health system, culminating in a multidimensional crisis. The deliberate destruction of vital infrastructure, such as hospitals, schools, housing, and public facilities, coupled with the deaths and injuries of medical personnel and support workers has only exacerbated the situation and further highlighted the existing gaps. This unprecedented catastrophe proves the criticality of adopting a new national inclusive integrated approach to meeting the immediate and long-term needs of the population. In this perspective, we explore the recovery roadmap features for rebuilding the health system in Gaza, specifically focusing on determining the primary challenges that might emerge, the trajectory of recovery, and the expected crisis scenarios. The existing evidence and perspectives of key stakeholders, including state and non-state health authorities in Palestine were synthesised. Despite some local and international initiatives undertaken to generate a concrete road to recovery, there remains a need for realistic, innovative, and comprehensive Marshall plans to rebuild Gaza's health system. The article draws on insights and gaps in current efforts and underscores the urgent need to address the challenges of rebuilding the health system. The authors strive to offer an inclusive and realistic path with the potential scenarios toward recovery and resilience considering the mass levels of loss and damage, and ways to move forward for building back a resilient health system in Gaza.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".