Knowledge, attitude and practice regarding biomedical waste management amongst healthcare workers in a teaching hospital from a north eastern state of India
Bibliographic record
Abstract
Background: Bio-medical waste (BMW) means any waste, which is generated during the diagnosis, treatment or immunization of human beings or animals or in research activities or in the production or testing of biological or in any health camp activities. Proper management of BMW ensures protection of public health and environment against any adverse effect associated with such waste materials. Several studies have reported that health care workers lack adequate level of awareness and right attitude regarding proper BMW management which ultimately reflects as incorrect practice of handling and disposal of bio medical waste. This study aimed to assess the knowledge, attitude and practices of healthcare workers regarding bio-medical waste management.Methods: This study was conducted at Tomo Riba Institute of Health and Medical Sciences (TRIHMS), Arunachal Pradesh, India. Hospital based cross sectional study was conducted and questionnaire were administered to 313 healthcare workers of TRIHMS who consented to participate in the study. A predesigned questionnaire for knowledge, attitude and practice study was used for data collection. Data was analysed using Microsoft Excel and STATA 13.Results: Study results show that the average knowledge score was highest amongst nurses (10±2.6) and least in class IV staffs (7.2±1.9). Amongst all participants laboratory technicians were mostly average or poor on the attitude score. Overall only 23 percent (n=73) of the healthcare workers were found to be performing good BMW management practice.Conclusions: Our study revealed that there is significant variation in knowledge, attitude, and practice regarding biomedical waste management among healthcare workers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".