Types of Hospital Waste and Waste Generation Rate in Different Hospitals of Faisalabad City, Pakistan
Bibliographic record
Abstract
Hospital waste has been one of the major problems in underdeveloped and developing countries in recent times. The present study is an attempt to analyze hospital waste generation of Faisalabad city. Forty four hospitals were selected out of which five were public, two were semi-government, six were trust and thirty one were private hospitals with a minimum capacity of ten beds. It was very difficult to acquire exact data related to the waste generated by hospitals as these health care centers were not following the international standards to handle waste generation. The primary data were collected through questionnaire, formal and informal meetings, interviews with the hospital staff and through personal observations. The secondary data were collected from the office of the Executive District Officer Health and Environment Protection | department, Faisalabad. Data analysis showed that about 7646 kg/day waste was generated by these hospitals out of which 6529 kg (85.40%) was non-infectious and 1117 kg (14.60%) was infectious waste. The government hospitals’ waste generation rate was 1.51 kg/bed/day, semi government 1.49 kg/bed/day, trust hospitals rate was 1.29 kg/bed/day and private hospitals 0.99 kg/bed/day. The overall waste generation rate of the hospitals of the study area was 1.28 kg/bed/day. It was recommended that the hospital staff must be trained to handle hospital waste so that the garbage should not create problems to human health.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".