Exploring the awareness level of biomedical waste management: Case of Indian healthcare
Bibliographic record
Abstract
This study aims to investigate the awareness level of Biomedical waste managements in healthcare facilities, and their perception among hospital waste management team, doctors, nurses, lab technicians and waste handlers in Northwest Delhi region in India.The study has been conducted through a questionnaire survey followed by the descriptive statistical analysis method.Questionnaire contains of 38 questions, where the first section deals with the hospital waste management team, the second section is for doctors, nurses and lab technicians, and the third section is for the waste handlers.Out of 311 respondents, there were 16 hospital waste management teams, 81 doctors, 92 nurses, 49 lab technicians and 73 waste handlers.It was surprising that only 40% (n=10) hospitals had any kind of waste treatment & disposal facility onsite, only 10% hospitals were using the latest technology and 60% hospitals shred the Biomedical waste before disposal.It was good to see that none of the hospital waste managements disposed the waste with general waste, and 40% of them were exhausting through government agencies and the remaining 60% were using private agencies to dispose the waste.Finally, all the hospitals maintained the record of waste generated.It is concluded that there was a lack of awareness about the biomedical waste generation, legislation and management among healthcare personnel, and they all needed regular audits and training programs at all levels, and a proper management starting from waste generation to its disposal at sites.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".