Assessment of health-care waste generation and its management strategy in the Gaza Strip, Palestine
Bibliographic record
Abstract
Abstract The situation of health-care waste in the Gaza Strip was threatening the environment and the public health due to the absence of appropriate health-care waste (HCW) handling, treatment, and disposal. In 2016, the total amount of HCW generated was estimated about 7199 kg day−1. Around 20% of the wastes was infectious, and the on-site segregation was done only for sharps in most health care facilities, while other infectious wastes were comingled with noninfectious normal wastes. In 2017, a new strategy for the health-care waste management (HCWM) was adopted. The strategy stated the necessity to segregate the HCW into three categories at the generation source to sharps, infectious wastes, and noninfectious wastes. The strategy was implemented over 40 clinics. The proper on-site segregation of the infectious and sharps showed that 2.4 kg day−1 and 0.7 kg day−1 of wastes is generated from UNRWA and Ministry of Health (MOH) clinics, respectively. This generation quantity accounts for a rate of 11 g per outpatient at UNRWA clinics and a ratio of 9.5 g per outpatient at MOH clinics. These quantities account for 33% and 54% of the total waste from UNRWA and governmental clinics in South and Middle 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".