Hazard Awareness & Practices of Biomedical Waste Management among healthcare Staff in Apex Hospitals: A Case Study in District Faisalabad
Bibliographic record
Abstract
Purpose: The waste produced in the course of healthcare activities carries a higher potential for infection and injury than any other type of waste. Design/Methodology/Approach: Using a qualitative approach, the study was conducted in two Apex hospitals i.e. Allied Hospital and District Head Quarter hospital Faisalabad, Punjab, Pakistan from 05 August to 15 October 2019. Consuming a semi-structured interview guide two focus group discussions (FGD) were conducted in each hospital and the participants were conveniently recruited. Each group was consisting of eight members who were directly involved in the creation and handling of biomedical waste (BMW). The thematic analysis method was used to analyze the data. Findings: The sanitary staff had insufficient knowledge about BMWM and about the BMWM/HCWM rule (2005) Pakistan. Also, there was no proper mechanism of training of the staff regarding waste management mean. While BMW was being disposed of according to BMWM rule (2005) Pakistan. Implications/Originality/Value: There is a weak mechanism of implementing proper BMWM in the hospitals where no training, no accountability, and no punishment was being executed against the violation of BMWM Rule 2005 Pakistan. So, a strict policy is required for its implementation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".